Explainable AI for Transparent Healthcare Decisions

Prof. Alexander wong


 Federated learning has emerged as a distributed machine learning paradigm for training models across decentralized edge devices while preserving data privacy and security. This review paper surveys federated learning methods, including federated averaging, secure aggregation, and differential privacy techniques. It explores the applications of federated learning in healthcare, IoT, and edge computing, along with privacy challenges and regulatory considerations.


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