JASAL: A remote patient health monitoring system driven by federated learning

Abstract

Remote patient monitoring (RPM) is growing fast, but most systems today rely on centralizing sensitive health data in the cloud. This raises serious concerns: patient privacy, strict regulations (such as GDPR and HIPAA), and “data silos,” in which hospitals can’t learn from one another without sharing private records. To address this, this thesis introduces JASAL—a privacy-preserving federated learning framework for real-time anomaly detection in distributed healthcare networks. Instead of moving patient data to a central server, JASAL trains small, lightweight Deep Autoencoders directly on edge devices (like IoT gateways at hospitals or clinics). Only encrypted model updates, not raw patient data, are sent to a coordinator for aggregation. The Hybrid Differential Privacy – Secure Multi-party Computation (DP-SMPC) of the JASAL system provides a dual level of privacy protection. The differential privacy level (ϵ = 1.0) provides protection from the published output of the learning task by concealing individual contributions of all patients participating; and the secure multi-party computation (SMPC) provides confidentiality of the models’ updates by protecting the contributions of each hospital from all other hospitals in the group. This defense-in-depth approach to physician and patient privacy ensures sensitive patient information will remain safe without sacrificing operational performance. In testing, JASAL performed well on a realistic synthetic dataset of 32,796 patients from 10 heterogeneous hospital nodes, achieving 96.28% overall diagnostic accuracy and an AUC-ROC of 0.978 with regard to its ability to reliably function in the detection of complex clinical conditions, such as sepsis, across multiple diverse clinical patient populations. Further, JASAL also exhibited a rather remarkable 98.3 percent reduction in bandwidth costs (from 20 MB to 1.2 GB) from transmitting raw data centrally to the hospitals while providing linear scalability (R² = 0.999) and ultra-low-inference latency (15.6 ms) sufficient for bandwidth-constrained, rural hospital networks. Tackling trust and usability of clinical artificial intelligence, JASAL’s lightweight Vectorized Attribution Engine produces very user-friendly feature level explanations (for example, ”Sepsis Pattern Detected: rising heart rate + falling SpO2”) in under 0.82 milliseconds, helping providers understand the basis of an alert. In addition, JASAL’s Adaptive Personalization Module automatically personalizes patientspecific thresholds to reduce alarm fatigue and to minimize false positives by as much as 49% compared to traditional static systems. Overall, JASAL demonstrates that strong privacy and high performance can coexist in real-world medical systems. It offers a practical, ethical, and scalable path toward truly autonomous, privacy-by-design remote patient monitoring.