Federated Learning for Privacy-Preserving Healthcare Wearable Security

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Author(s): Grace Shalini T. (SRM Institute of Science and Technology, India), Pratham Shrivastav (SRM Institute of Science and Technology, India)and Parthiv Gopa (SRM Institute of Science and Technology, India)
Copyright: 2027
Pages: 36
Source title: Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques
Source Author(s)/Editor(s): Manisha Guduri (Lawrence Technological University, USA), George Pappas (Lawrence Technological University, USA)and Sandeep Thota (Oracle Inc., USA)
DOI: 10.4018/979-8-3373-7426-0.ch005

Keywords: Health Information Systems / Information Security & Privacy / Medical Information Science Reference / Security & Forensics

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Abstract

The work demonstrates and performs a privacy-preserving multi-institutional multi-party end-to-end federated learning (FL) framework to detect security anomalies in multi-party data streams of healthcare wearables. The natural pipeline uses distributed non-identically-distributed (non-IID) telemetry to jointly train anomaly detectors on hospitals and device vendors across a secure environment without having to centralize raw patient data. The method includes time-varying deep neural network and client level differential privacy (DP-SGD), secure aggregation with efficient communication compressing update transmission. This work provides assumed yet reasonable multi-site outcomes to concretize feasibility, simulating what a production deployment would provide: on six institutions and 21,804 users generating 1.2 billion records, the DP-FL model achieves an AUROC of 0.943 ± 0.008 and an F1-score of 0.887 ± 0.011 when identifying the security -important anomalies such as spoofed sensors, tampered firmware, abnormal pairing, and suspicious connection events.

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Federated Learning for Privacy-Preserving Healthcare Wearable Security

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