Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Abstract

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.

arXiv commentACM Transactions on Computing for Healthcare, Vol. 3, No. 4, Article 40. Publication date: October 2022

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Cited by in corpus (4)

• Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
• Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
• Privacy-preserving machine learning for healthcare: open challenges and future perspectives
• Centralized vs Decentralized Federated Learning: A trade-off performance analysis