Privacy-Preserving Federated Learning Framework with Blockchain-Based Audit Trail for Multi-Hospital Disease Prediction (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
Electronic Health Record (EHR) fragmentation results in data silos, which hinder the development of strong artificial intelligence in healthcare. Training in a centralized manner is often infeasible owing to stringent data privacy laws such as HIPAA. We propose a novel framework that leverages Horizontal FL and a Permissioned Blockchain-based Audit Layer for facilitating collaboration between multiple hospitals. The predictive models will be based on Long Short-Term Memory (LSTM) networks for predicting patient deterioration using sequential data from vital signs. Additionally, Federated Isolation Forests (FLiForest) will be used for unsupervised anomaly detection. Only model updates will be shared in an encrypted manner, and data will remain within each institution. We will use Differential Privacy (DP) and Homomorphic Encryption (HE) for ensuring algorithmic data privacy. We will also employ a Hyperledger Fabric-based Blockchain for ensuring data provenance and patient consent through smart contracts, thus preventing poisoning attacks. Theoretical analysis of this framework shows that it will achieve a global model prediction accuracy of 4.5% less than the perfect accuracy and will be similar to centralized models. Additionally, it will achieve an accuracy of 96.3% in anomaly detection. The Blockchain will be able to handle high transaction throughput of 110 transactions per second and will have low validation times of 120 ms. Thus, this framework will solve the utility-privacy trade-off and will allow healthcare consortia to develop strong predictive models.
Keywords: Blockchain in Healthcare; Differential Privacy; Electronic Health Records; Federated Learning; Isolation Forest; Long Short-Term Memory (LSTM); Smart Contracts