Blockchain-Enabled Federated Learning for Privacy and Security

The book Blockchain-Enabled Federated Learning for Privacy and Security explores the integration of blockchain technology and federated learning to address critical challenges in healthcare data sharing. With the rise of electronic health records medical imaging IoMT devices and genomics safeguarding patient privacy while enabling collaborative AI has become essential. Blockchain provides decentralization immutability and trust while federated learning ensures model training without exposing raw data. Together they form a privacy-preserving auditable and scalable framework for healthcare AI. The book covers fundamentals system architectures cryptographic techniques and performance trade-offs along with real-world case studies in cancer research IoMT and COVID-19 diagnosis. It highlights regulatory and ethical considerations such as GDPR HIPAA and India's DPDP Act and proposes future research in quantum integration explainable AI fairness-aware FL and governance through smart contracts. This comprehensive guide serves researchers healthcare professionals and policymakers in building secure transparent and patient-centric healthcare ecosystems.