MULTI-LAYER PRIVACY PROTECTION FOR FEDERATED LEARNING IN HEALTHCARE

ARTIFICIAL INTELLIGENCE AND DATA SCIENCE

Federated Learning (FL) has surfaced as a robust approach for collaboratively training machine learning models without the necessity of sharing raw data. In sensitive fields such as healthcare, where the confidentiality of patient information is of utmost importance, it is essential to uphold data privacy while ensuring model accuracy. This paper introduces a Multilayer Privacy Protection (MPP) framework tailored for Federated Learning within healthcare systems. The framework amalgamates Secure Aggregation, Encryption, and Differential Privacy methodologies to establish layered defense strategies. Additionally, it incorporates user-focused design tools including Empathy Map, Mind Map, Project Charter, and Risk Assessment to guarantee ethical, practical, and dependable implementation. Through architectural modeling and experimental validation, the proposed framework illustrates improved data protection and model efficacy with minimal computational burden.

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