Privacy-Preserving Federated Intelligence for Healthcare IoT: A Secure Biomedical Engineering Framework for Real-Time Patient Monitoring
Main article
Abstract
Healthcare Internet of Things (H-IoT) deployments now stream continuous physiological data from millions of wearable devices, bedside monitors and implanted sensors, but the centralised machine-learning pipelines that analyse these streams expose patients to systemic privacy risk and single-point-of-failure attacks. We describe a secure biomedical-engineering framework that delivers real-time patient monitoring without exposing raw clinical signals. The framework couples on-device training of a Bidirectional Long Short-Term Memory (Bi-LSTM) anomaly detector to a federated averaging layer, which is in turn anchored to a permissioned Proof-of-Stake blockchain through PBKDF2-derived authentication keys, AES-GCM-encrypted gradients, and smart-contract-mediated aggregation. A key contribution is the explicit decoupling of the cryptographic verification path from the model-update path, which lets the system tolerate Byzantine clients without sacrificing convergence speed. We evaluate the framework on the public ToN-IoT and CICIDS2019 intrusion-detection corpora, treating the attack-classification task as a proxy for monitoring-stream integrity, and report a mean accuracy of 96.42 % on ToN-IoT and 97.38 % on CICIDS2019, an F1 of 0.969, an AUC of 0.985, a false-positive rate of 2.18 %, and a per-round end-to-end latency of approximately 5.2 s on a 10-validator network. An ablation isolates the contribution of each component: removing blockchain anchoring lowers accuracy by 3.26 percentage points and the security score from 98 to 72; removing PBKDF2 reduces accuracy by 0.91 points; removing on-device encryption collapses the security score to 41 even though detection accuracy is preserved. We further analyse the energy and scalability envelope of the consensus layer, showing that the Proof-of-Stake choice scales linearly with validator count up to 32 nodes whereas an equivalent Byzantine-Fault-Tolerant deployment scales quadratically. The framework is therefore a practical route to trustworthy, privacy-preserving, real-time biomedical analytics that satisfies HIPAA and GDPR audit requirements without sacrificing clinical responsiveness.
