Federated Learning for Internet of Medical Healthcare: Issues and Challenges

Abstract

Federated Learning is a decentralized machine learning method that allows collaborative model training across several devices or institutions while maintaining the privacy and localization of data. Since the raw data is used locally, this collaborative method enables the development of a strong and precise global model without jeopardizing the privacy and security of sensitive data. The healthcare sector is an important one that focuses on preserving and enhancing people's health through medical services, diagnoses, treatments, and preventative measures. Efficient evaluation of Federated Learning in the Internet of Medical Things (IoMT) enables breakthroughs in medical image analysis, electronic health record analysis, personalized treatment planning, and drug development by enabling institutions to train models locally on sensitive patient information without sharing raw data. This paper presents the role of Federated Learning in healthcare and current trends in Federated Learning-based healthcare. A case study is presented on deep Federated Learning for privacy-preserving in healthcare. Finally, challenges and future research directions are discussed in the paper.

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