Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics

Abstract

Advances in digital medicine necessitate widespread use of patient data by hospitals and medical institutions for analytics, clinical research, and training of intelligent healthcare systems. Against the backdrop of stringent privacy concerns, data-minimization principles, and the regulated nature of personal health data—especially healthcare providers cannot share data but can share model parameters or predictions—federated machine learning provides a promising solution to these pressing demands. The federated paradigm not only protects patient privacy but also mitigates concerns of data leakage and breach; yet it raises new concerns about data governance and security, requiring that the centralized server merely holds model parameters and does not learns from the data.

A system architecture, illustrated via a use-case example, integrates data-privacy guarantees and system-level security with technical tools from federated analytics. Key techniques not only cover the major data-analytic tasks identified for healthcare but also embody principles of opening up non-independent and identically distributed health data while still being safe against leakage. Introduction and conclusion delineate the wider significance of these privacy-preserving works and the remaining research gaps, pointing toward evaluation of federated algorithms with explainable-area-under-risk metrics and defense mechanisms against arbitrary-label attacks.