Federated Learning for Privacy-Preserving Healthcare Analytics

Abstract

Federated learning enables collaborative model training across distributed healthcare institutions without sharing patient data. This chapter presents a privacy-preserving federated framework with differential privacy guarantees, secure aggregation protocols, and personalization mechanisms for non-IID healthcare datasets. Experiments on electronic health records demonstrate superior performance over baseline approaches.

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