Blockchain-Based Federated Learning Enables Secure Medical Collaboration
This systematic review (Wang et al., J Med Internet Res, 2026) analyzes over 100 studies from 2018–2025 on blockchain-based federated learning (BCFL) in medicine. It finds BCFL combines blockchain's decentralized trust with federated learning's privacy-preserving training to mitigate model tampering, data leakage, and incentive gaps across EHR sharing, IoMT, epidemic forecasting, and telemedicine. The review categorizes architectures and outlines practical trade-offs and challenges.
Key Points
- 1Demonstrates BCFL integrates blockchain immutability with federated learning for privacy-preserving cross-institutional model training
- 2Addresses model tampering, data leakage, and incentive shortcomings, improving trust and auditability in collaborative medical AI
- 3Suggests architectures and incentives guide deployment choices for EHR, IoMT, epidemic forecasting, and telemedicine applications
Scoring Rationale
Comprehensive peer-reviewed synthesis with practical guidance, but limited novelty beyond aggregating existing BCFL studies and experiments.
Sources
Public references used for this report.
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