Federated learning with update level noise perturbation and blockchain audit logging for privacy-preserving healthcare ECG classification

Summary

Researchers propose FedChainDP, a federated learning framework for ECG classification across hospitals that adds update-level Gaussian noise, encrypts updates with AES-256-GCM, and logs them on a blockchain with Merkle-tree integrity protection. On MIT-BIH data split across 5 hospitals, the model reached 98.91% ± 0.14% accuracy, but the authors state the evaluation used a beat-level random split with duplication-based balancing, and no formal (ε, δ)-differential privacy guarantee is claimed.