The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems worldwide transition toward data-driven paradigms for precision medicine, clinical decision support, and population health management, the exponential growth in medical data generation—from electronic health records (EHRs) and medical imaging to genomic sequencing and Internet of Things (IoT)-enabled continuous monitoring—presents unprecedented opportunities for advancing health care through machine learning (ML) [1-3].

Federated learning (FL) has emerged as a promising paradigm for collaborative model training across distributed health care institutions while maintaining data locality [4,5]. However, standard FL architectures face challenges, including model poisoning, gradient leakage, and coordination failures [6-8]. Blockchain technology offers complementary capabilities through immutable audit trails, decentralized trust, and automated smart contract execution [9], yet integration with FL in health care introduces unique scalability, efficiency, and regulatory compliance challenges.

This tutorial addresses a critical gap by providing a structured, cross-disciplinary framework that integrates blockchain with FL in the context of health care, with explicit coverage of medical data taxonomies, clinical workflows, and regulatory compliance.

While prior surveys have explored the integration of FL and blockchain applications in health care and beyond [10-13], this tutorial differs in scope and emphasis. In particular, we provide (1) a systematic medical data taxonomy that classifies diverse health care data types (EHRs, imaging, genomics, biometrics, Internet of Medical Things [IoMT], clinical trials, etc) and analyzes their specific FL requirements; (2) a set of three integration architectures (fully coupled, semicoupled, and loosely coupled) with detailed evaluation of security, scalability, and compliance tradeoffs tailored to health care deployments; (3) comprehensive treatment of advanced cryptographic defenses (zero-knowledge proofs [ZKPs], homomorphic encryption [HE], and differential privacy [DP]) with health care–specific vulnerability analysis; and (4) a structured regulatory compliance framework mapping Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and United States Food and Drug Administration (FDA) guidelines to blockchain-FL system design. Moreover, while prior surveys have typically focused on high-level concepts, our tutorial adopts a more comprehensive and tutorial-oriented scope by combining blockchain fundamentals, FL methodologies, health care–specific applications, security analysis, and forward-looking research directions. This extensive focus allows us to introduce a comprehensive, clinically oriented, and compliance-aware framework that integrates FL and blockchain for health care, providing actionable insights for both researchers and practitioners implementing secure, privacy-preserving, and regulation-compliant collaborative analytics systems. Table 1 contrasts this tutorial with earlier blockchain-based FL (BCFL) surveys and frameworks to clarify the contributions.

Contemporary health care faces a fundamental paradox: the increasing need for large-scale, collaborative data analytics to advance medical knowledge is juxtaposed with stringent privacy regulations and the inherent sensitivity of medical data. Traditional centralized approaches to health care ML create critical vulnerabilities where patient information aggregated in single repositories becomes an attractive target for sophisticated attacks [14,15]. Recent breaches affecting millions of patient records underscore the inadequacy of current security models, particularly when regulatory frameworks, such as HIPAA and GDPR, and emerging national data protection laws impose severe penalties for data mishandling [16].

While FL addresses data locality concerns by enabling distributed model training, it introduces health care–critical vulnerabilities. Model poisoning attacks could compromise diagnostic accuracy [6,7], potentially leading to misdiagnosis or incorrect treatment recommendations. Gradient leakage exploits can enable patient reidentification from shared model updates [8,17], violating privacy guarantees that health care institutions must maintain. Coordination failures in distributed learning processes could disrupt critical clinical workflows [18], affecting real-time decision support systems.

Blockchain technology addresses these vulnerabilities through immutable audit trails that ensure accountability, decentralized trust establishment that eliminates single points of failure, automated smart contract execution for verifiable protocol compliance, and comprehensive data provenance tracking [9]. However, health care–specific integration challenges remain, including scalability constraints when processing high-frequency medical data updates from IoMT devices, energy efficiency requirements for sustainable long-term deployment in resource-constrained clinical environments, compliance with medical device regulations for AI/ML systems, and real-time performance requirements for clinical decision support applications.

Existing BCFL approaches [19-23] are predominantly designed for generic data types and fail to address health care–specific requirements. Critical gaps include a lack of regulatory compliance frameworks for HIPAA and GDPR, insufficient integration with clinical workflows and existing health IT systems, inadequate support for multimodal medical data with heterogeneous privacy requirements, and absence of explainability mechanisms required for medical decision-making. This tutorial fills these gaps by providing a comprehensive framework tailored specifically for health care applications.

This tutorial advances the state-of-the-art at the intersection of blockchain technology, FL, and health care informatics through several methodological and theoretical contributions:

This tutorial is designed for researchers and practitioners at the intersection of health care informatics, distributed systems, and ML. Our target audience includes (1) health care informaticians seeking to implement privacy-preserving collaborative analytics, (2) computer scientists developing secure FL systems, (3) blockchain researchers exploring health care applications, (4) clinical researchers interested in multi-institutional collaborations, and (5) regulatory and policy experts working on health care data governance. The tutorial assumes familiarity with basic ML concepts but provides a comprehensive background on FL and blockchain technologies. We emphasize practical implementation considerations while maintaining rigorous technical depth appropriate for publication in top-tier venues.

The remainder of this tutorial is structured as illustrated in Figure 1. The Medical Data and Applications in ML section introduces a comprehensive taxonomy of medical data types, including EHRs, imaging, genomics, and sensor data, and discusses their relevance to ML. The AI and FL in Health Care section explores the evolution of AI in health care, highlighting FL paradigms, their challenges, and future trends. The Blockchain section reviews fundamental blockchain concepts, such as consensus, data structures, and cross-chain communication, with a focus on health care applicability. The Integration of FL With Blockchain section presents integration architectures that combine FL and blockchain, along with their security, privacy, and regulatory implications in health care. The Empirical Validation and Performance Evaluation section synthesizes quantitative evidence from clinical deployments, benchmarks, and adversarial robustness studies to provide empirical validation, assess performance tradeoffs, and establish the framework’s real-world feasibility. The Related Work section offers a critical review of previous work on the integration of blockchain and FL to contextualize the contributions of this tutorial. The Future Research Directions section synthesizes emerging research challenges and opportunities, grouped under cryptographic resilience, infrastructure scalability, health care–specific consensus and incentives, and regulatory compliance in system integration. Finally, the Conclusion section summarizes the key findings and reflects on the real-world translation of BCFL systems into clinical practice.

Medical data encompass a diverse array of information crucial for understanding, diagnosing, and treating various health conditions. These data, ranging from patient demographics and medical history to diagnostic images and genomic sequences, hold immense potential for advancing health care through ML applications. By harnessing the power of ML algorithms, medical data can be analyzed to extract valuable insights, predict patient outcomes, personalize treatments, and optimize health care delivery. However, the utilization of medical data for ML requires careful consideration of data storage and management practices to ensure compliance with privacy regulations, maintain data integrity, and facilitate seamless access for research and clinical purposes. This section explores the different types of medical data and their applications in ML.

EHRs are digital patient charts containing medical history, diagnoses, medications, treatment plans, immunization dates, allergy information, medical images, and lab results, providing a comprehensive, up-to-date view for informed decision-making [24,25]. The key EHR features include the following: (1) digital format, electronic records replace paper for easier storage, access, and sharing; (2) interoperability, designed for sharing among different providers and organizations, facilitating better care coordination; (3) real-time access, authorized professionals get quick access to critical information, which is crucial for emergencies; (4) patient engagement, features allow patients to access data, schedule appointments, and communicate with providers; (5) decision support, tools offer alerts for drug interactions, screening reminders, and clinical guidelines; and (6) data security and privacy, security measures protect confidentiality, with access restricted to authorized personnel [26]. EHRs are a rich source of structured and unstructured data for ML applications. ML algorithms analyze this comprehensive patient information to extract insights, predict outcomes, identify patterns, and improve clinical decision-making [27,28]. In ML, EHR data are used for tasks, including the following: (1) predictive analytics, where models forecast medical events (eg, hospital readmission, disease onset, and mortality) for proactive, personalized care [29,30]; (2) disease identification and diagnosis, where algorithms assist in early detection by identifying subtle patterns and anomalies, aiding accurate and timely diagnoses [31,32]; and (3) treatment recommendations, where models suggest personalized plans based on history, demographics, genetics, and past responses to optimize outcomes [33].

Medical imaging data encompass diverse visual representations of internal body structures, acquired via distinct imaging modalities [34,35] for clinical scrutiny, diagnosis, and ongoing assessment. The key components of medical imaging data are (1) patient information, (2) imaging modalities, (3) image files, and (4) reports. First, patient information includes basic demographic and identifying details (eg, name, ID, age, and gender). Second, among imaging modalities, each modality uses specific physical principles [36] to generate images adapted to clinical needs. The imaging modalities include (1) radiography for projection imaging of osseous structures [37]; (2) computed tomography (CT) for high-resolution cross-sectional imaging using x-ray and computational reconstruction [38]; (3) magnetic resonance imaging (MRI) for detailed soft tissue visualization via magnetic fields and radiofrequency pulses [39]; (4) ultrasonography for real-time acoustic wave imaging, which is ideal for soft tissues and dynamic processes [40]; (5) nuclear medicine imaging for visualizing organ function via radiopharmaceuticals [41]; (6) positron emission tomography for quantitative functional imaging, especially in oncology, neurology, and cardiology [42]; and (7) fluoroscopic imaging for dynamic, real-time x-ray visualization during interventional and functional studies [43]. Third, regarding image files, medical data, including metadata (patient information, acquisition parameters, and image details) [44], are stored, shared, and transmitted according to technical standards [45], notably Digital Imaging and Communications in Medicine (DICOM) [46]. Fourth, reports include narrative documents generated by radiologists summarizing image analysis findings for the treatment planning of referring physicians. Medical imaging data have a wide range of ML applications in health care [47], including the following: (1) disease diagnosis and classification, ML algorithms assist in the diagnosis and classification of diseases like cancer and neurological, cardiovascular, and musculoskeletal disorders [48]; (2) computer-aided detection, computer-aided detection systems use ML to help radiologists detect abnormalities (eg, tumors, lesions, and fractures), improving diagnostic accuracy and efficiency [49]; (3) image-based biomarker discovery, ML identifies imaging biomarkers associated with diseases or treatment responses, which is valuable for prognosis, efficacy assessment, and personalized medicine [50,51]; (4) treatment planning and monitoring, imaging data are used to develop personalized plans, and ML predicts outcomes, monitors progression, and optimizes treatment strategies [52]; (5) image reconstruction and enhancement, ML techniques (eg, deep learning) improve image quality from various modalities (MRI, CT, and ultrasonography) by reducing artifacts and enhancing resolution for better interpretation [53]; (6) image registration and fusion, ML algorithms automatically align and combine multiple images (from different modalities or time points) for comprehensive visualization and analysis; and (7) drug discovery and development, ML analyzes imaging data to evaluate drug effects on disease progression, identify potential drug targets, and optimize delivery methods [54].

Genomic data pertain to information concerning the organization and operation of the genome within an organism [55,56]. In health care ML, this refers to comprehensive information from an individual’s genome (entire DNA/genetic composition), offering profound insights into genetic predispositions, treatment responses, and overall health [57]. The key components for medical use cases include the following: (1) genome sequences, the complete set of genetic material (DNA), consisting of nucleotides (A, T, C, and G) that encode genetic information; (2) genes, specific DNA sequences that encode instructions for protein synthesis, including their location, structure, and function; (3) single nucleotide polymorphisms, single-nucleotide variations that can influence traits, disease susceptibility, and drug responses, along with their associated phenotypic and disease information [58]; (4) copy number variations, genomic alterations involving changes in the number of copies of DNA segments, affecting gene dosage and leading to phenotypic variations and disease susceptibility [59]; (5) gene expression profiles, information on the activity levels of different genes (often generated via microarrays or RNA sequencing [60]), providing insights into protein synthesis; (6) epigenetic modifications, heritable changes in gene expression (eg, DNA methylation and histone modifications) that do not alter the DNA sequence but influence gene activity and phenotype [61]; and (7) genetic variation databases, compilations of genomic data, genetic variants, annotations, and associated phenotypic information from various sources (eg, population studies and disease databases) [62]. These components are essential for understanding the genetic basis of diseases, identifying risk factors, and developing personalized health care approaches. The utilization of genomic data in health care ML includes the following: (1) disease prediction and risk stratification, ML algorithms scrutinize genomic data to discern patterns and variations linked to specific diseases, assessing associated health risks [63]; (2) personalized medicine, genomic data form the basis for personalized treatment plans [64,65], and ML models predict individual medication responses for tailored strategies [66-68]; (3) drug discovery and computational genomics, ML analyzes genomic data to expedite discovery, identifying drug targets and comprehending genetic underpinnings for more efficacious therapeutic solutions [69]; (4) genomic counseling, ML algorithms decipher complex genomic data, assisting health care professionals in communicating intricate genetic details, risks, and familial implications [70]; (5) early detection and diagnostics, integrating genomic data and ML algorithms facilitates early detection by discerning subtle genetic variations indicative of specific conditions, enabling timely intervention [71,72]; (6) research and population health informatics, aggregated genomic data subjected to ML can advance the understanding of disease genetic foundations at the population level, informing public health initiatives and epidemiological studies [73]; and (7) genomic sequencing and computational analysis, ML plays a crucial role in interpreting extensive genomic datasets from sequencing technologies, identifying genetic mutations, variations, and other health-impacting information [74].

Biometric data in the medical context refer to unique physical or behavioral characteristics used for individual identification and monitoring. These data are crucial in health care for patient identification, medical record access control, personalized treatments, and health condition monitoring. The components of biometric data include the following: (1) physiological biometrics, data types like fingerprints, facial recognition, iris/retina scans, DNA, smell recognition, and hand geometry data [75]; (2) behavioral biometrics, data types such as voice recognition, gait analysis, and typing dynamics data [75]; (3) health-related biometrics, data types such as heart rate, blood pressure, blood oxygen levels, electrocardiography (ECG) patterns [76], and brain wave patterns (electroencephalography [EEG] patterns) [77]; and (4) analytical biometric technologies, advanced modalities like microbial biometrics, which analyze unique microbiome compositions for identification and health assessment [78], and olfactory biometrics, which utilize distinctive body odor profiles for identification and disease detection [79].

Biometric data integrated into health care ML encompass quantifiable physiological or behavioral attributes [75,80] for accurate identification [81,82], secure access [83,84], and health assessment [85]. The primary applications of biometric data in health care ML include the following: (1) biometric identification and access control, biometric features (eg, fingerprints and facial characteristics) are pivotal for precise identification and heightened security, governing access to sensitive areas, EHRs, and medical devices [80,86]; (2) patient identification and record matching, biometric identifiers (eg, fingerprints and iris scans) ensure precise linkage of patients to their health records, minimizing errors, with ML algorithms enhancing matching accuracy and elevating care quality [87-90]; (3) biometric monitoring for health assessment, continuous monitoring of biometric data (eg, heart rate and ECG data) via wearables/sensors facilitates real-time health assessment, and ML analyzes dynamic data for the early detection of anomalies, supporting timely intervention and personalized management [91-93]; (4) behavioral biometrics for mental health monitoring, behavioral biometrics (eg, typing and voice modulation) contribute to assessing mental health and detecting behavioral changes [94,95], and ML models discern patterns indicative of conditions, aiding targeted interventions [96]; (5) biometric data in clinical trials, biometric data are used for participant identification, monitoring, and data integrity, and ML assists in efficient management and analysis, ensuring study validity [97,98]; (6) voice and speech analysis for diagnostics, ML algorithms process voice patterns to detect potential markers for conditions like Parkinson disease [99,100] or respiratory disorders [101,102], contributing to diagnostic capabilities [103]; and (7) facial recognition for patient monitoring, facial recognition is used for monitoring patient well-being and distress, and ML analyzes facial expressions, offering insights into comfort levels and enhancing care quality [104,105].

Sensor data in medical contexts represent information collected from various devices (wearable, implantable, or environmental devices) to monitor health, detect condition changes, and assist diagnoses. These data continuously track physiological parameters, activity levels, and environmental conditions. The components of medical sensor data include the following: (1) wearable sensors, data from devices like heart rate monitors, blood pressure monitors, blood glucose monitors, activity trackers, and pulse oximeters; (2) implantable sensors, data from devices like cardiac monitors, glucose monitors, and intraocular pressure sensors; (3) environmental sensors, data gathered from air quality monitors (for pollutants and allergens affecting respiratory conditions) and temperature/humidity sensors; (4) specialized medical sensors, data from ECG, EEG, electromyography, gait sensors, and sleep monitors; and (5) smart health homes, data extracted from fall detectors and bed sensors that monitor sleep patterns, bed occupancy, and vital signs.

Sensor data used in health care ML are collected from various sources (wearables, medical equipment, and monitoring tools) [106,107], providing real-time health and activity insights. ML algorithms analyze these data to make predictions, identify patterns, and offer personalized insights. The utilization of sensor data in health care ML includes the following: (1) vital sign monitoring, (2) wearable devices for activity tracking, (3) blood glucose monitoring and continuous glucose monitoring, (4) ECG monitoring, (5) sleep monitoring, (6) environmental sensors, (7) medication adherence monitoring, (8) fall detection and activity recognition, and (9) biometric sensors for stress and emotion monitoring. First, in vital sign monitoring, ML analyzes continuous vital sign data (heart rate, blood pressure, etc) to detect anomalies, predict health deteriorations, and offer early warnings [108,109]. Second, regarding wearable devices for activity tracking, ML models analyze data from accelerometers and gyroscopes to assess physical health, detect abnormalities, and provide personalized insights for fitness and rehabilitation [110]. Third, in blood glucose monitoring and continuous glucose monitoring, ML algorithms analyze continuous glucose monitoring data to predict glucose level trends, recommend insulin dosages, and enhance diabetes management [111]. Fourth, in ECG monitoring, ML interprets ECG data to identify cardiac abnormalities, predict cardiovascular risk, and recommend early interventions [112,113]. Fifth, in sleep monitoring, ML algorithms analyze sleep data (from wearables and bed sensors) to identify disorders, provide insights into sleep hygiene, and recommend personalized interventions [114,115]. Sixth, regarding environmental sensors, ML correlates environmental data (air quality and temperature) with health outcomes, aiding in identifying triggers for respiratory conditions or allergies [116]. Seventh, in medication adherence monitoring, ML analyzes adherence patterns tracked by smart sensors, sending reminders and providing providers with compliance insights [117,118]. Eighth, regarding fall detection and activity recognition, ML models use motion sensor data for fall risk assessment, accident prediction, and adapting care plans for individuals with mobility challenges [119]. Ninth, regarding biometric sensors for stress and emotion monitoring, ML analyzes biometric signals (eg, skin conductance and heart rate variability) to provide insights into mental health, stress management, and emotional well-being [120,121].

Patient-generated data (PGD) in health care ML refer to health-related information actively contributed by patients [122,123], distinct from traditional clinical records. Sourced directly via wearables, mobile apps, and patient-reported outcomes (PROs), PGD foster a patient-centric, data-driven approach, enabling personalized interventions, early detection, and improved patient-provider communication [124]. Beyond sensor data, PGD utilization in health care ML includes the following: (1) mobile health apps and surveys, patients input health information and feedback via apps, and ML processes these data to derive insights into treatment effectiveness, medication adherence, and satisfaction, informing personalized care plans [125]; (2) social media and online communities, patients share health experiences and concerns online [126], and ML conducts analyses on social media data for health-related trends, sentiment, and public health monitoring, contributing to population health research and patient perspectives [127,128]; (3) genomic and genetic data sharing, patients voluntarily contribute genetic information for research, and ML analyzes aggregated genomic data to identify genetic factors associated with diseases, fostering precision medicine advancements [129]; and (4) telehealth and virtual visits, patients offer health updates during virtual consultations, and ML algorithms analyze PGD from these visits to support clinical decision-making, monitor treatment progress, and enhance virtual health care quality [130].

Clinical trial data refer to information systematically collected during trials designed to evaluate the safety, efficacy, or effectiveness of new medical interventions (drugs, devices, and procedures) in human participants [131]. These data, governed by a structured protocol, inform medical decision-making, regulatory approvals, and medical knowledge advancements. Integrating these data into ML models enables the development of predictive algorithms, risk assessment tools, and decision support systems, contributing to evidence-based medicine, personalized treatments, and enhanced clinical research efficiency [132]. The key components of clinical trial data are as follows: (1) demographic information, (2) informed consent, (3) medical history, (4) intervention details, (5) clinical assessments, (6) adverse events, (7) efficacy endpoints, (8) follow-up data, and (9) protocol deviations. First, demographic information includes details on study participants (age, gender, race, and ethnicity) incorporated into ML models to assess intervention response across groups and develop personalized treatment plans. Second, informed consent involves documentation confirming that participants were informed of risks/benefits and voluntarily agreed to participate, ensuring ethical standards and regulatory compliance, with ML incorporating this to restrict the analysis to consented data [133,134]. Third, medical history includes information on pre-existing conditions, relevant history, and concurrent medications, which is used to identify comorbidities that may impact treatment outcomes. Fourth, intervention details include specifics about the product/procedure (dosage, administration, and protocol), with ML analyzing these details to identify patterns associated with treatment success or failure, predicting efficacy in future cases [135]. Fifth, clinical assessments include physical examinations, lab tests, imaging, and measurements to assess the health status and intervention response, with ML processing this information to identify trends, correlations, or anomalies indicative of treatment responses or adverse events, aiding early detection and prediction [136]. Sixth, adverse events involve records of side effects experienced, including severity and relation to the intervention, with ML learning from these historical data to predict the likelihood of adverse events for new interventions, supporting risk assessment and proactive management [137,138]. Seventh, efficacy endpoints include measurements (eg, symptom relief and disease marker improvement) used to determine intervention effectiveness, with ML analyzing these data to develop predictive models for treatment success/failure and identify key contributing factors. Eighth, follow-up data include information on long-term outcomes, adherence, and sustained effects collected during posttrial visits, which is essential for longitudinal analyses, with ML using this information to predict the long-term effects of interventions, including sustained efficacy or potential relapse [139]. Ninth, protocol deviations involve documentation of deviations from the original plan and their reasons, with ML analyzing the data to identify how deviations impact study outcomes, allowing for adjustment or prediction of their potential effects [140].

Prescription and medication data in medical records relate to prescribed drugs, dosage, frequency, and related details. These data are crucial for patient safety, medication management, monitoring public health and treatment efficacy, research, regulatory compliance, and facilitating communication. Integrated into EHRs, they provide a comprehensive medication history, allowing health care professionals to make informed decisions and avoid potential adverse events. Aggregated and anonymized data are used in research to broadly assess medication safety and effectiveness. The key components of prescription and medication data are as follows: (1) patient information; (2) prescriber information; (3) prescription date; (4) medication name; (5) dosage, frequency, and route of administration; (6) duration of treatment; (7) instructions for use; (8) refill information; (9) allergies and contraindications; (10) adverse reactions; (11) medication changes; (12) medication discontinuation; and (13) medication administration records (MARs). First, patient information includes identification details (name, date of birth, and demographics), which are used in ML for personalized treatment. Second, prescriber information includes details about the prescribing provider, with ML models being trained to recognize prescribing practices associated with positive patient outcomes [141]. Third, prescription date is the date the prescription was issued, and temporal analysis assists in predicting medication adherence and treatment outcomes, with ML identifying patterns related to issuance timing and patient behavior [142]. Fourth, medication name involves generic and brand names, with ML models categorizing medications by therapeutic class, aiding in identifying commonalities in treatment outcomes [143]. Fifth, dosage, frequency, and route of administration include the prescribed amount/strength, how often it should be taken, and the administration method, which are crucial details for predicting adherence and adverse events, with ML models identifying optimal regimens [144]. Sixth, duration of treatment is the prescribed period, with ML models analyzing this to predict long-term outcomes, including treatment success and potential drug resistance [145]. Seventh, instructions for use include additional guidance (eg, taken with food and specific times), with natural language processing (NLP) approaches extracting insights from free-text instructions to identify nuances in patient guidance [146]. Eighth, refill information includes details on authorized refills, which are essential for predicting adherence and persistence, with ML identifying factors influencing refill behavior and predicting discontinuation likelihood [147]. Ninth, allergies and contraindications involve records of known patient allergies or contraindications, with ML identifying associations among these records, adverse drug reactions, and specific medications to predict safety and suitability for individual patients [148]. Tenth, adverse reactions involve documentation of experienced side effects, with data being analyzed to develop predictive models identifying patients at higher risk for specific side effects, enabling proactive management. Eleventh, medication changes include information about dosage adjustments or switches, with ML analyzing historical changes to predict future treatment modifications, assisting in personalized planning [129,149]. Twelfth, medication discontinuation involves the reason and date of stopping medication, with ML models predicting factors contributing to discontinuation, helping providers intervene to improve adherence [150]. Thirteenth, MARs include records of actual medication administration in a health care setting, with MAR data being used to train ML models for predicting administration patterns and identifying deviations from the prescribed regimen [151].

Laboratory data in medical records are the results of tests and analyses conducted on patient samples, which are essential for diagnosing, monitoring, and managing various medical conditions. The types of data collected vary by patient presentation and provider assessment. ML techniques (supervised, unsupervised, and deep learning) are applied depending on the nature of the data and analysis goals. The common types of laboratory data are the results of the following tests: (1) blood tests, (2) urinalysis, (3) microbiology tests, (4) pathology tests, (5) hematology tests, (6) immunology tests, (7) endocrine tests, (8) serology tests, (9) genetic tests, and (10) radiology and imaging studies. First, blood tests include assessments of the complete blood count (cell counts), blood chemistry panel (electrolytes, glucose, and organ function tests), and lipid profile, with ML models identifying patterns in complete blood count results associated with specific diseases (eg, anemia and infections) [152]. Second, urinalysis involves examination of the physical/chemical properties of urine, with ML algorithms detecting patterns indicative of kidney disorders, urinary tract infections, or diabetes [153]. Third, microbiology tests involve identifying microorganisms and their antibiotic sensitivity, with ML assisting in microorganism identification from culture data and predicting antibiotic susceptibility for personalized treatment [154,155]. Fourth, pathology tests include tissue biopsy and cytology (cell examination) performed for disease/abnormality diagnosis (eg, cancer), with image recognition ML models trained on pathology slides assisting in identifying abnormal tissue or cancerous cells [50,156]. Fifth, hematology tests include coagulation studies (blood clotting) and erythrocyte sedimentation rate (an inflammation measure), with ML models analyzing coagulation data to predict the risk of bleeding or clotting disorders [157]. Sixth, immunology tests include antibody tests (detecting immune system antibodies) and viral load (measuring the virus in blood), with ML algorithms identifying patterns in antibody levels to diagnose autoimmune or infectious conditions [158,159]. Seventh, endocrine tests include assessments of hormone levels (eg, thyroid and insulin), with ML models analyzing these levels to predict and monitor endocrine disorders like thyroid dysfunction or diabetes [160,161]. Eighth, serology tests include analyses of serum components (proteins, enzymes, and electrolytes), with ML identifying markers associated with specific diseases, aiding early detection and monitoring [158]. Ninth, genetic tests involve the identification of specific genetic markers for condition diagnosis, with ML analyzing these data to identify disease risk, predict treatment response, or diagnose genetic disorders [70]. Tenth, radiology and imaging studies include diagnostic tests contributing to medical data, with image recognition/segmentation ML models being trained on radiology images to assist in diagnosing conditions like tumors or fractures.

Telehealth data refer to information collected during remote health care interactions between patients and providers [162]. These data are crucial in modern health care, utilizing technology (video, phone, and online platforms) to deliver services remotely and contributing to the overall patient record [163]. The key components of telehealth data are as follows: (1) audio and video recordings, (2) text-based communication, (3) diagnostic and monitoring device data, (4) EHR integration, (5) appointment and scheduling data, (6) patient demographics and consent, (7) prescription and medication data, and (8) PROs. First, audio and video recordings include recordings of virtual consultations used for documentation and quality assurance, with ML analyzing these for sentiment or clinical insights, speech recognition transcribing audio for verbal analysis, and facial recognition/sentiment analysis assessing patient emotions and engagement [164]. Second, text-based communication involves chat logs, texts, or emails containing symptom and treatment information, with NLP extracting and categorizing information (symptoms and treatment discussions) and sentiment analysis gauging patient satisfaction and well-being [165]. Third, diagnostic and monitoring device data include data from remote devices (eg, blood pressure monitors, glucose meters, and wearables) that enable continuous remote health monitoring, with ML models analyzing trends and patterns, and time series analysis and predictive modeling detecting trends/anomalies in vital signs and predicting exacerbations of chronic conditions [166,167]. Fourth, EHR integration involves integration into EHRs/electronic medical records for a comprehensive patient view, with ML analyzing combined data to identify correlations between in-person and virtual interactions, improving diagnosis and treatment planning. Fifth, appointment and scheduling data include information on scheduling, duration, and attendance, with analysis optimizing appointment availability and patient access, and predictive analytics optimizing scheduling and resource allocation by anticipating demand. Sixth, patient demographics and consent include details about the patient and consent for services, ensuring privacy compliance and personalized care context, with ML analyzing demographic data for population health trends, targeted interventions, and service personalization. Seventh, prescription and medication data include information on prescriptions, management, and adherence, supporting virtual refills, with predictive modeling analyzing adherence patterns to inform personalized interventions and identifying medication-related risks and interactions [168]. Eighth, PROs include patient-reported data on symptoms, well-being, and effectiveness, with ML analyzing PROs via text mining and sentiment analysis to predict treatment responses and assess correlations with clinical outcomes to inform decisions [169].

While raw medical data provide the foundation for downstream ML applications, they often require substantial transformation to become analytically useful. Feature engineering refers to the process of extracting, selecting, and constructing meaningful features from raw data to enhance model accuracy, interpretability, and generalizability [170]. This step is particularly critical in the health care domain due to the inherent heterogeneity, noise, and sparsity of clinical data.

The feature engineering process encompasses several essential techniques tailored to different data modalities. Normalization and encoding transform categorical variables and scale numerical values to comparable ranges. Temporal aggregation captures time-dependent patterns from longitudinal records such as EHR data. NLP embedding methods convert unstructured clinical notes into dense vector representations. Signal processing techniques extract meaningful patterns from physiological sensors and medical imaging. Dimensionality reduction methods address the curse of dimensionality while preserving informative variance in high-dimensional genomic or imaging data.

Effective feature engineering not only improves model performance but also enhances interpretability and supports reproducibility across diverse health care settings [171]. In FL scenarios, harmonizing feature spaces across distributed clients is crucial to ensure compatible model training and aggregation [171]. Figure 2 illustrates a high-level pipeline that uses systematic preprocessing transformations for mapping heterogeneous raw medical data (detailed taxonomy in Figure 3) to structured feature vectors suitable for downstream ML and FL modeling.

AI is revolutionizing health care, impacting diagnosis, treatment, and patient care. This section explores the evolution of AI in health care, from its historical roots to cutting-edge developments, showcasing its potential across various health care domains. We will further delve into FL as a key technology for the future. FL’s collaborative approach enables privacy-preserving data analysis, making it a powerful tool for health care research and delivery. We will discuss its applications, case studies, current challenges, and future directions.

AI is transforming health care across a wide spectrum, from disease diagnosis to patient care management, making significant contributions in several key areas (Figure 4). The first area is disease diagnosis and imaging. AI has revolutionized medical imaging, providing more accurate and faster diagnoses [172]. Deep learning models, trained on large datasets (eg, x-ray images [173], MRI scan images [174], and CT scan images [175]), can identify patterns undetectable to the human eye, aiding in the early detection of diseases like cancer, cardiovascular abnormalities, and neurological disorders. The second area is drug development and personalized medicine. AI algorithms streamline drug development by predicting molecular behavior [176] and identifying potential drug candidates [177]. In personalized medicine, AI analyzes patient data, including genetic information, to tailor treatments, improving efficacy and reducing side effects [123,178]. The third area is predictive analytics in patient care. AI’s predictive analytics are crucial to preventive medicine. EHRs are analyzed [179] to predict patient risks for diseases [180], hospital readmission [181], and adverse events, enabling proactive care [180]. The fourth area is robotics and surgical assistance. AI-integrated robotics improve surgical precision and outcomes [182]. AI-driven robots assist surgeons in complex procedures, reducing human error and recovery time, and AI supports surgeon training through virtual reality simulations [183]. The fifth area is patient engagement and telemedicine. AI-powered chatbots and virtual health assistants provide 24/7 support and health monitoring [184], enhancing patient engagement and adherence. In telemedicine, AI tools assist in remote diagnosis and consultation, making health care more accessible [185]. The sixth area is health care administration and management. AI streamlines administrative tasks (scheduling, billing, and claims) [186] and optimizes hospital operations, resource allocation, and patient flow, improving overall efficiency and reducing costs [187]. The seventh area is global health and epidemic response. AI is pivotal in tracking and predicting the spread of infectious diseases [188]. During the COVID-19 pandemic, AI models were instrumental in analyzing virus transmission [189], assessing vaccine development [190], and managing health care resources [191]. The key application domains of AI in health care are presented in Figure 5.

Blockchain technology has undergone a remarkable evolution since its inception in 2009 with the creation of Bitcoin by an individual or group using the pseudonym Satoshi Nakamoto [270]. The primary purpose of Bitcoin was to establish a decentralized digital currency, and the innovation that made this possible was blockchain—a distributed ledger that records transactions across a network of computers securely and transparently.

In the following years, the potential applications of blockchain technology expanded beyond cryptocurrency. Vitalik Buterin introduced Ethereum [271] in 2015, introducing the concept of smart contracts—self-executing contracts with the terms of the agreement directly written into code. This development opened up a broader spectrum of decentralized applications (DApps) and laid the foundation for blockchain’s role in facilitating not only peer-to-peer (P2P) transactions but also complex programmable interactions.

The years that followed witnessed a surge in blockchain projects and platforms, each aiming to address specific challenges across various industries. The technology gained recognition for its potential to enhance transparency, security, and efficiency. Consortia and collaborations emerged, with enterprises exploring how blockchain could optimize supply chains [272-275], streamline financial transactions, and enhance data integrity.

Blockchain continues to evolve, with ongoing efforts to address scalability issues, energy consumption concerns, and regulatory considerations. From its humble beginnings as the underlying technology for Bitcoin, blockchain has grown into a versatile tool with the potential to reshape how industries manage and verify data. The technology’s journey reflects an ongoing quest for innovative solutions to long-standing challenges in the digital realm.

In this section, we provide a concise overview of fundamental concepts, features, structure, and taxonomy within the realm of blockchain technology.

Blockchain technology is a decentralized and distributed ledger system designed to facilitate secure and transparent transactions without the need for a central authority. At its core, blockchain consists of a chain of blocks, each containing a list of transactions. These blocks are linked together in a chronological and immutable manner, forming a continuous chain. One of the key features of blockchain is its decentralization, meaning that the ledger is maintained by a network of nodes rather than a single central entity. This distributed nature enhances security, reduces the risk of fraud, and ensures transparency in the transaction process.

Blockchain technology possesses several distinctive features that contribute to its popularity and versatility across various industries. The features of blockchain technology are as follows: (1) decentralization, (2) immutability, (3) transparency, (4) security, (5) distributed ledger, (6) consensus mechanism, (7) anonymity and privacy, (8) efficiency and speed, (9) interoperability, and (10) ability to support smart contracts. First, regarding decentralization, blockchain operates on a P2P network, eliminating the need for a central authority or intermediary, and this enhances security, reduces the risk of a single point of failure, and promotes trust among mutually untrusted participants [276]. Second, immutability is the capability of a blockchain ledger to remain unchanged. Once a block is added to the blockchain, it becomes virtually impossible to alter or delete the information within it. Immutability ensures the integrity of the transaction history and builds trust in the accuracy of recorded data. Third, regarding transparency, the entire transaction history is visible to all participants in the network, and this fosters trust and accountability as participants can independently verify transactions and the state of the blockchain. Fourth, regarding security, blockchain uses cryptographic techniques to secure transactions and control access to the network. Consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS), enhance security by preventing unauthorized changes to the blockchain [277-279]. Fifth, the ledger is distributed among the nodes over the network, and each node in the network holds a copy of the blockchain. This distribution ensures redundancy, resilience, and a shared source of trust among the participants. Sixth, consensus is a mechanism that gives the network the ability to agree upon the validity of transactions (and blocks) and the order in which they can be added to the blockchain. Seventh, regarding anonymity and privacy, while all transactions in the blockchain network are transparent, participants remain pseudonymous due to the use of public/private key pairs. Eighth, regarding efficiency and speed, blockchain reduces the need for intermediaries and manual processes, leading to faster and more efficient transactions. In some cases, however, the speed of transactions may depend on the specific consensus mechanism used. For instance, PoW blockchains, like Bitcoin, tend to be slower (ie, with less throughput) compared to traditional payment systems like Visa and Mastercard. The primary reasons for this include the inefficiency of the underlying consensus mechanisms and the way transactions are processed [280]. Ninth, blockchain interoperability refers to the capacity of various blockchain networks to interact seamlessly, facilitating the exchange of messages, data, and tokens among them [281-285]. Standards and protocols are evolving to enable such communication and data exchange between disparate blockchain platforms. The Inter-Blockchain Communication (IBC) protocol [286] is designed to facilitate this interoperability by providing a standardized way for independent blockchains to transfer and communicate with each other. Tenth, regarding the ability to support smart contracts, smart contracts are self-executing contracts with the terms directly written into code. These contracts automate and enforce predefined rules and agreements, reducing the need for intermediaries and streamlining processes [287,288].

Consensus in the context of blockchain refers to the mechanism by which a distributed network of nodes agrees on the state of the system or the validity of transactions [289-291]. Since blockchain operates in a decentralized and trustless environment, consensus is crucial to ensure that all participants have a consistent view of the blockchain’s history and current state. The consensus mechanism is responsible for preventing double-spending (where the same digital asset is spent more than once) and maintaining the integrity of the blockchain. Different blockchain networks use various consensus algorithms, each with its own set of rules and processes. The most prominent and currently existing consensus protocols are as follows: PoW; PoS; Byzantine fault tolerance (BFT); direct acyclic graph tangle (DAG); and hybrid consensus, including PoS and PoW hybrid, proof of authority (PoA) and PoW hybrid, delegated PoS (DPoS) and PoA hybrid, PoW and BFT hybrid, and hybrid voting system.

In blockchain technology, the data structure plays a pivotal role in ensuring the integrity, security, and immutability of the distributed ledger [278,311]. At its core, a blockchain is composed of a series of blocks, each containing a bundle of transactions. These blocks are cryptographically linked together sequentially, forming a continuous chain. The data structure of a block typically includes several key components: a header, a list of transactions, and a cryptographic hash. The header contains metadata, such as the block’s unique identifier (block hash), a timestamp, and a reference to the previous block’s hash, thus establishing the chronological order of blocks. The transaction list records the details of all transactions included in the block, such as sender and receiver addresses, transaction amounts, and cryptographic signatures for verification. Additionally, each block is assigned a cryptographic hash, which is computed based on its contents using a hashing algorithm like SHA-256. This hash serves as a unique identifier for the block and is crucial for maintaining the integrity of the blockchain. Any alteration to the data within a block would result in a change in its hash, thereby breaking the chain’s continuity and signaling tampering. The inherent immutability and tamper-resistance of the data structure in blockchain ensure that once recorded, transactions cannot be altered or deleted without consensus from the network participants, establishing a reliable and transparent system for recording and verifying transactions [312].

The network architecture in blockchain is a distributed and decentralized system that enables the secure and transparent exchange of data and value across a network of interconnected nodes. At its core, a blockchain operates as a P2P network where each participant, or node, maintains a copy of the entire blockchain ledger. This distributed architecture ensures that there is no single point of failure, as the data are replicated and synchronized across multiple nodes. Nodes communicate with each other through a consensus mechanism. Depending on the consensus algorithm used, nodes may take on different roles, such as miners in the PoW system or validators in the PoS system. Transactions are broadcast to the network and validated by consensus, typically requiring confirmation from a majority of nodes before being added to the blockchain. This network architecture provides several benefits, including resilience against censorship and tampering, increased transparency and accountability, and enhanced security through cryptographic techniques.

Additionally, the decentralized nature of blockchain networks promotes trust among participants by eliminating the need for intermediaries and central authorities, thereby fostering a more inclusive and democratic ecosystem for conducting transactions and exchanging value [313].

Blockchain technology typically consists of 6 common layers, each serving a specific purpose in the network’s function and security (Figure 7). The six layers are as follows: (1) network layer, the foundation facilitating communication between nodes using protocols like TCP/IP, HTTP, and P2P protocols, responsible for transmitting data (transactions and blocks) across the network; (2) data layer, stores the actual blockchain data, including blocks, transactions, and smart contracts, utilizing structures and storage mechanisms optimized for secure and efficient retrieval; (3) consensus layer, ensures all nodes agree on the validity and order of transactions added to the blockchain using various mechanisms, such as PoW, PoS, DPoS, and pBFT; (4) smart contract layer, enables the creation and execution of programmable, self-executing contracts with terms written directly into code, powering various DApps; (5) incentive layer, provides mechanisms (typically block rewards and transaction fees, eg, Bitcoin’s block rewards and Ethereum’s gas fees) to incentivize participants (miners or validators) to contribute resources and maintain the network’s security and integrity; and (6) application layer, encompasses the user-facing applications and interfaces (eg, DApps, wallets, smart contracts, and other software) that interact with the blockchain protocol.

Interoperability is one of the most important features of next-generation blockchain networks and refers to the ability of different blockchain platforms to communicate, share data, and transact with each other seamlessly. It enables interoperability between disparate blockchain networks, allowing them to interact and exchange information or assets without the need for intermediaries or centralized exchanges. Interoperability is essential for realizing the full potential of blockchain technology by facilitating cross-chain transactions, asset transfers, and data sharing between different blockchain ecosystems [286].

The IBC protocol is a set of standards and protocols designed to enable communication and interoperability between independent blockchain networks. IBC facilitates the secure and trustless transfer of assets and data across different blockchains, allowing them to interact and transact with each other directly. The protocol defines a standardized messaging format and a set of rules for validating and verifying transactions between participating blockchains. By implementing the IBC protocol, blockchain networks can establish interconnectivity, enabling cross-chain transactions, decentralized exchanges, and interoperable DApps [283,314,315].

IBC is a pillar of the Internet of Blockchains [311,316], a vision where blockchain networks are interconnected like the global internet, creating a decentralized and open ecosystem where data, assets, and services flow freely between different blockchains. Examples of interoperability solutions include the following: (1) Cosmos, a decentralized network utilizing the IBC protocol to enable communication and interoperability between its various blockchain platforms, with the Cosmos Hub serving as the primary connection point [317]; (2) Polkadot, a multichain blockchain platform that enables interoperability between different parachains (parallel blockchains) within its network via its relay chain, allowing them to share data and assets seamlessly [318]; and (3) Wanchain, a cross-chain blockchain platform focused on interoperability, enabling the secure and decentralized exchange of assets between different blockchain networks, including Bitcoin and Ethereum [319].

Integrating blockchain technology with FL has emerged as a novel approach to address inherent data privacy, security, and trust challenges within distributed ML systems [21,23,332,333]. FL, characterized by training ML models across decentralized devices without centrally aggregating raw data, offers significant advantages in preserving user privacy and data confidentiality. In other words, in federated ML, the exchange occurs at the parameter level instead of transmitting raw data. This approach mitigates the risk associated with centralized computing architectures, which are susceptible to targeted attacks and potential denial of service due to their single-point-of-failure vulnerability. A detailed discussion on FL is presented in the FL Details section, including its different types (HFL, VFL, and FTL), applications (particularly in health care), and potential future directions (integration with advanced analytics and cross-institutional collaborations), highlighting FL’s pivotal role in reshaping health care technology.

However, concerns persist regarding the integrity of FL systems, particularly regarding data tampering or manipulation by malicious or compromised nodes. Blockchain, renowned for its immutable and transparent ledger capabilities, presents a compelling solution to these challenges. By leveraging blockchain’s decentralized consensus mechanisms and cryptographic primitives, FL systems can ensure data integrity, traceability, and transparency throughout the ML model training process [334-336]. Moreover, blockchain’s smart contract functionality enables the establishment of auditable and self-executing agreements among participants, further enhancing the trustworthiness of FL collaborations [23]. This integration not only addresses privacy and security concerns but also fosters a more collaborative and inclusive environment for distributed ML research and applications. As such, the motivation behind the integration of blockchain and FL lies in the pursuit of enhancing data privacy, security, and trust in decentralized ML ecosystems, ultimately advancing the adoption and efficacy of FL methodologies in various domains such as health care [13,336,337].

Blockchain technology holds immense potential to significantly enhance the security, transparency, and trustworthiness of FL systems by addressing key challenges like verifying local model updates, aggregating the global model, and incentivizing participants. The integration of blockchain into FL is explored as follows: (1) verifying local model updates, (2) global model aggregation, and (3) incentivizing participants. First, regarding verifying local model updates, blockchain offers an immutable and tamper-proof record for local model updates from participants. These updates are recorded as transactions, ensuring their validity and preventing unauthorized modifications. Furthermore, smart contracts can be used to define specific rules, and they function as verification mechanisms, executing algorithms to validate the integrity and accuracy of updates [338]. Second, regarding global model aggregation, blockchain empowers FL with decentralized consensus mechanisms (eg, PoW and PoS) that allow participants to collectively agree on the process of aggregating the global model. The entire aggregation process is transparently recorded on the blockchain, allowing participants to verify the fairness and accuracy of the final model [339]. Third, regarding incentivizing participants, a key strength of blockchain is its ability to create tokenized incentives (tokens or cryptocurrencies) to reward participants who contribute data or computational resources [340]. Smart contracts automate the distribution of these incentives based on predefined criteria (eg, quality of contribution and computational resources provided) [341]. Crucially, the entire distribution process is transparently recorded on the blockchain, ensuring accountability and fairness [342].

By leveraging the unique strengths of blockchain technology, FL systems can achieve a new level of security, transparency, and trust, ultimately fostering a more collaborative and efficient environment for AI development.

Several research efforts have identified distinct architectures for integrating blockchain and FL. This paper proposes a framework similar to that in a previous report [343], with slight variations in terminology. Based on the level of interaction between blockchain and FL entities, we categorize these architectures as fully coupled, semicoupled, and loosely coupled.

This section grounds our BCFL integration in evidence from clinical deployments, consortia-scale studies, and controlled benchmarks. We synthesize outcomes along four axes: (1) accuracy versus centralized baselines; (2) scalability and latency/throughput under permissioned consensus; (3) robustness and privacy under realistic threat models; and (4) compliance, provenance, and operational constraints. Where possible, we report representative ranges from cited studies rather than single-point claims.

Table 6 summarizes emblematic deployments. Across drug discovery (MELLODDY [253]), neuro-oncology (federated tumor segmentation [390]), pandemic diagnostics [432], and IoMT monitoring [251,433], BCFL consistently delivers accuracy within approximately 2%-3% of centralized training while providing auditability and coordination via permissioned chains or distributed ledgers.

Controlled experiments report stable training under 50‐200 participants with permissioned consensus, and recent empirical studies demonstrate that blockchain-enabled FL can reduce communication overhead by 40%‐60% through communication-aware aggregation schemes [339,344]. Specifically, there was a 43% communication overhead reduction and a 37% lower computational cost while maintaining 95.2% model accuracy [344]. Sharded or committee-based designs sustain 80-120 TPS at the consortium scale while keeping the consensus overhead under 5% for ≤50 nodes, and at 100 nodes, PBFT-style protocols exhibit 12%-15% overhead [438,439].

Against gradient inversion and membership inference, combining DP (eg, ϵ<0.1) with encrypted or masked updates sustains utility within 3%‐5% of nonprivate baselines at an approximately 2.8 times compute overhead, while Byzantine-robust aggregation maintains <5% degradation under up to 30% compromised clients [19,352]. Recent work has demonstrated that blockchain-enabled frameworks maintain robustness against multiple adversarial attack vectors with accuracy levels above 93% [344]. For verifiable compliance, ZKPs amortize to proof generation of 8‐15 s and verification of 2‐4 s per minute-scale update [440].

Empirical audits indicate technical adherence to approximately 93% of HIPAA/GDPR provisions, with 70%‐80% manual audit effort reduction when provenance is automated via smart contracts [395]. For FDA transparency obligations on AI/ML devices, immutable lineage reduces evidence cycles from an initial 6‐9 months to 2‐3 months in reported pipelines [397]. Operational deployment of blockchain-enabled FL systems faces several infrastructure challenges, including maintaining high system availability, managing blockchain storage growth as institutions scale, balancing computational overhead from cryptographic operations, and ensuring adequate network bandwidth for model parameter exchange [9,441].

In permissioned health care networks, PoA achieves 200-300 TPS and approximately 95% lower energy than PoW, whereas PBFT offers stronger Byzantine resilience at 40%‐50% lower throughput [442]. Comparative syntheses show that BCFL improves privacy metrics by 40%‐60% over vanilla FL while staying within approximately 3% of centralized accuracy [344,384]. Recent systematic reviews covering literature from 2023‐2024 confirm these patterns across diverse health care applications [12,400]. Fully-coupled blockchain-FL designs minimize latency (reduction of 30%‐40%) but demand 2‐3 times the resources, while semicoupled architectures offer a balanced middle ground [443].

In this section, we comprehensively explore existing research endeavors focusing on integrating BCFL frameworks within health care contexts. Initially, we delve into the corpus of literature dedicated to elucidating the practical applications, methodologies, and outcomes of utilizing blockchain technology in conjunction with FL for health care use cases. In addition, we aim to provide a taxonomy and categorize the existing work based on the insights garnered throughout this paper. To achieve this, we classify the research according to integration architecture (ie, fully coupled, semicoupled, and loosely coupled), blockchain platform, FL type, and data type. Furthermore, we highlight their primary contributions as well as their limitations. Later, we examine existing surveys and reviews focusing on blockchain-assisted FL in health care. This will provide insights into the overall research landscape and help identify any gaps or areas for further exploration.

Samantray and Reddy [444] proposed a hybrid blockchain architecture with quantum key encryption for Healthcare 5.0. Bhasker et al [445] presented a Healthcare 5.0 smart health care system for addressing confidentiality, trust, and compliance standards for safe health monitoring. Bhardwaj et al [428] introduced a privacy-preserving Federated Blockchain Explainable Artificial Intelligence Optimization (PPFBXAIO) framework for the integration of blockchain and XAI, and optimization techniques to ensure privacy, traceability, and robustness in FL-based systems. Ali et al [446] presented a ZKP and HE approach for EHR security. Das et al [447] presented a meta-learning approach for improved model generalization.

Chen et al [19] proposed a trustworthy and fair blockchain-based FL framework to address challenges like malicious attacks (model/data poisoning) and free-rider behavior. In another study [448], a quality-aware BCFL with a secure key-sharing mechanism to protect local model parameters and ensure data security was proposed. Mazid et al [436] proposed a blockchain-driven FL framework for secure health care services using IoMT devices. Liang et al [449] proposed a software architecture integrating FL and blockchain to mitigate bias and fairness issues in health care predictive modeling while safeguarding patient privacy. Om Kumar et al [450] introduced a mechanism to reward organizations participating in the FL process, ensuring privacy-preserving model transfer using loosely-coupled integration with the Ethereum blockchain. Moulahi et al [437] integrated FL and blockchain to develop a trusted system for predicting diabetes risk while ensuring data privacy and model integrity.

Ali et al [451] focused on integrating blockchain with FL for secure and decentralized analysis of electronic medical records in precision medicine. Chang et al [442] proposed an integration of adaptive DP and gradient verification-based consensus protocols in a fully-coupled architecture for health care analytics. Lian et al [452] presented a blockchain-based personalized FL system for ensuring security and privacy in IoMT.

Farooq et al [453] developed an automated system for analyzing patients’ live data within a fully-coupled architecture, while Zhang et al [454] proposed a blockchain-enabled FL framework for health care data privacy protection. Aich et al [336] introduced a blockchain-assisted FL framework for personal data preservation, and Passerat-Palmbach et al [335] proposed a novel architecture for FL integrated with blockchain within health care systems. A taxonomy of existing research studies, along with additional relevant work, has been compiled in Table 7.

To the best of our knowledge, comprehensive surveys or reviews focusing on the integration of blockchain and FL for health care use cases are scarce. While individual studies have explored the potential of each technology independently within health care settings, there is a notable lack of resources delving into the synergistic benefits and challenges of combining blockchain’s immutable ledger capabilities with FL’s decentralized model training approach.

Ngoupayou Limbepe et al [12] presented a taxonomy of FL-based privacy mechanisms categorized into privacy-enhancing technologies and hybrid techniques. This survey integrates privacy-enhancing technologies with blockchain in FL frameworks for smart health care systems. Noteworthy initial explorations, such as those conducted by Myrzashova et al [13] and Nguyen et al [464], offer valuable insights but often lack a broader perspective. Myrzashova et al [13], for instance, analyzed the advantages and disadvantages of BCFL integration in health care, but they overlooked the importance of a data type taxonomy. Understanding the diverse medical data types (eg, genomics and imaging) used in health care ML is crucial, as different data may have varying security and privacy requirements. Similarly, Nguyen et al [464] introduced a new conceptual architecture that integrates blockchain and AI for combating the COVID-19 pandemic. While offering valuable insights into addressing specific challenges posed by the pandemic, its scope is limited to COVID-19 and related data, lacking a broader analysis of the integration’s potential for various health care use cases beyond this specific context. Hemdan et al [465] examined the convergence of digital twin technology, blockchain, and FL in the medical field, with a specific focus on their technical architecture and real-world applications. Orabi et al [400] presented a literature review of BCFL applications in health care for protecting sensitive data. This paper investigated how integrating FL with blockchain can enhance its security, performance, and reliability.

In contrast to these existing reports, this tutorial offers a more comprehensive perspective. We present a taxonomy of medical data used for ML, providing a foundational understanding of the diverse data types relevant to this integration. Furthermore, we unveil an innovative architecture meticulously designed for the seamless integration of blockchain and FL within health care systems, addressing the need for secure and privacy-preserving health care analytics.

The integration of FL and blockchain in health care introduces a transformative paradigm for secure, privacy-preserving, and collaborative medical AI systems. Despite notable theoretical advances, several research frontiers remain underexplored. These can be broadly categorized into 4 interrelated areas: cryptographic foundations and quantum-resilient privacy, scalable and interoperable infrastructure, health care–specific consensus and incentivization, and full-stack integration with regulatory automation.

A core requirement for the secure deployment of BCFL systems in health care is the development of advanced cryptographic mechanisms that ensure long-term privacy, data integrity, and resilience against quantum adversaries. Postquantum cryptographic primitives (such as lattice-based schemes like CRYSTALS-Kyber and Dilithium, and hash-based schemes like SPHINCS+) must be integrated into the BCFL stack to secure signatures and encryption layers [466]. In parallel, ZKPs and zero-knowledge virtual machines offer promising approaches to verifiably audit FL operations and regulatory compliance without exposing raw medical data. Furthermore, HE schemes, particularly multikey variants of the CKKS (Cheon-KimKim-Song) cryptosystem [467], enable secure aggregation of encrypted model parameters from multiple health care institutions without revealing individual contributions. The substantial computational overhead of such operations can be alleviated through specialized hardware accelerators, notably smart network interface cards, which offload cryptographic computations to dedicated processing units, thereby improving efficiency and scalability [468]. Complementarily, DP mechanisms require refinement to handle medical-specific feature types, with research needed into adaptive noise strategies and budget allocation tailored to data sensitivity. Together, these innovations will enable secure, auditable, and privacy-preserving collaborative learning in health care environments.

As clinical systems generate increasingly voluminous and heterogeneous data, BCFL infrastructure must address throughput, latency, and interoperability challenges to support real-time learning and inference [401,439]. Health care–specific sharding architectures (such as parallel blockchains for EHRs, medical imaging, and genomics) can significantly improve throughput by distributing model updates across application domains. Complementary to this, layer-2 scaling solutions, including rollups [469] and state channels, offer reductions in transaction costs and ledger bloat, making frequent model updates feasible without congesting the blockchain [470]. Additionally, cross-chain interoperability mechanisms, such as those based on the Cosmos IBC protocol or Polkadot’s relay chain model, are essential for enabling collaboration among health care institutions operating on heterogeneous blockchain platforms. Moreover, edge-centric FL will become increasingly important as IoMT and wearable devices proliferate. Furthermore, split learning and energy-efficient blockchain clients will be crucial to bring BCFL capabilities to constrained edge nodes while preserving security guarantees. These advancements will enable a scalable and interoperable ecosystem capable of supporting global health care collaborations.

Generic blockchain consensus protocols are ill-suited for the privacy, latency, and regulatory demands of health care. There is a growing need to design health care–oriented consensus protocols, such as medical and practical BFT [471], which can minimize communication overhead and incorporate privacy-aware authorization and medical rule validation into the consensus process. Similarly, reputation-based BFT [472] can ensure the credibility of participating institutions and practitioners, dynamically adjusting voting power based on behavior and contribution history. To sustain active participation in FL processes, especially among resource-constrained or data-rich stakeholders, incentive mechanisms must be tailored to health care environments. The proof-of-federated-work paradigm introduces an innovative strategy to quantify participant contributions using metrics like local accuracy, data diversity, and compliance adherence [473]. These are integrated into token-based or hybrid compensation schemes, further reinforced by game-theoretic models that optimize incentives based on Nash equilibrium to deter free-riding and encourage high-quality model updates. Such mechanisms are foundational to the long-term viability of decentralized health care learning networks.

For BCFL frameworks to be deployed at scale, they must integrate seamlessly with health care systems, satisfy regulatory requirements, and maintain usability in real-time clinical contexts. Smart contracts should evolve to support automated, upgradeable compliance verification that is capable of adapting to dynamic legal frameworks such as HIPAA, GDPR, and FDA AI guidelines [474]. By incorporating ZKP-based attestations, institutions can demonstrate compliance without revealing sensitive patient-level operations [440]. Simultaneously, embedding explainable AI techniques, such as SHAP values and attention heatmaps, into the FL pipeline is essential to ensure clinician trust and support regulatory transparency [475]. Algorithmically, innovations in personalized FL, multitask learning (eg, MOCHA [476]), and community-based learning [477] can help address patient heterogeneity and improve predictive power in distributed environments. Real-time FL capabilities, triggered by event-driven smart contracts (eg, emergency alerts), are crucial for responsive applications like ICU triage and chronic condition monitoring [432]. To ensure long-term sustainability, quantum-resistant blockchain architectures should be explored using crypto-agile protocols and hybrid classical/postquantum stacks. These systems must also support interoperability, modular compliance, and secure cross-border data flows, forming the backbone of globally federated, privacy-aware health care AI infrastructure.