A Robust Framework for Federated Learning in Privacy-Preserving Health Informatics
Keywords:
Federated Learning, Health Informatics, Privacy, Secure Aggregation, Medical AI, Data HeterogeneityAbstract
As the volume of patient health data grows, the need for privacy-preserving machine learning in healthcare becomes critical. Federated Learning (FL) provides a promising approach by enabling decentralized training without raw data exchange. This paper proposes a robust framework tailored to privacy-preserving health informatics by addressing key challenges such as data heterogeneity, communication efficiency, and security threats. The proposed model leverages secure aggregation, adaptive optimization, and bias mitigation to ensure robust and fair learning outcomes. Experimental results and a thorough literature review support the efficacy and relevance of our framework in real-world medical applications
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