Federated Learning: Issues in Medical Application

TL;DR

Federated learning enables AI training across distributed medical data without data sharing, but faces challenges like data heterogeneity, client management, and security, which require further research and development of flexible frameworks.

Contribution

This paper reviews current issues in federated learning for medicine and introduces a modular framework to test solutions for these challenges.

Findings

Identification of key issues: data heterogeneity, client management, security.

Development of a modular federated learning framework for experimentation.

Plans to release the framework publicly after completion.

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.