Enhancing Security in Medical Data Sharing with Federated Learning Approaches
Utilizes federated learning techniques to enable privacy-preserving sharing of medical data across healthcare institutions
Keywords:
Federated learning, Privacy-preserving, Medical data sharing, Healthcare, Machine learningAbstract
Federated learning is a distributed machine learning approach that enables model training across multiple decentralized edge devices or servers holding local data samples without exchanging them. This technique has gained significant attention in healthcare for its potential to enable privacy-preserving sharing of sensitive medical data across institutions. This paper provides an overview of federated learning in the context of medical data sharing, discussing its benefits, challenges, and applications. We also present a case study of federated learning implementation in healthcare and discuss future research directions.
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