Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

TL;DR

This survey reviews federated learning in healthcare, highlighting its applications, challenges, and methods for developing privacy-preserving machine learning models across distributed medical data sources.

Contribution

It provides a comprehensive overview of existing research, applications, and challenges of federated learning specifically tailored for healthcare industry needs.

Findings

Identifies key challenges in healthcare federated learning.

Summarizes various applications across medical domains.

Highlights methods to address data privacy and security.

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.