Federated Learning: Issues in Medical Application

Abstract

Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.

Survey of Medical Applications of Federated Learning

“…According to previous literature reviews on FL [ 15 – 17 , 20 ], heterogeneity and security concerns were frequently discussed. Therefore, we explored the extent to which studies addressed these issues.…”

“…Review papers on FL in the medical domain have been published [ 15 – 19 ]; however, these studies have only introduced a limited number of examples of medical FL research. Our study differs from existing FL reviews by concentrating on specific instances of medical FL research.…”