29
- Volume 1, Issue 2 2025
By Umna Iftikhar, Ramzan Ali Butt
10.21621/ijec.20250102.03
Keywords: Healthcare, Blockchain, Federated Learning, IPFS, Consensus Algorithm.
Blockchain technology offers transformative potential for healthcare through its decentralized, immutable ledger system. When it combined with federated learning, which allows the models to trained collaboratively without sharing data these technologies address the critical challenges in medical, data privacy and security. Our architecture demonstrates how this combined implementation (1) secures patient records via cryptographic hashing, (2) maintains data integrity across distributed nodes, and (3) preserves confidentiality during processes, while specifically addressing three key implementation challenges: computational overhead from dual-layer encryption, and evolving regulatory compliance requirements. Through comparative evaluation of blockchain types (public, private, consortium), we identify trade-offs in scalability versus control, with consortium models showing optimal balance for healthcare applications (processing 800-1200 transactions/sec in trials). Federated learning implementations reduced data transfer needs by 40% compared to centralized alternatives, though model convergence times increased by 25-35% due to healthcare data heterogeneity. The framework provides practical guidance for healthcare organizations adopting these technologies, including governance models for cross-institutional collaboration and standardized approaches for meeting HIPAA/GDPR requirements through privacy-preserving smart contracts. While demonstrating 60% improvement in security metrics, the analysis acknowledges persistent challenges in node synchronization latency and the need for specialized hardware to maintain performance in large-scale deployments.