From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare

TL;DR

This review analyzes recent federated learning studies in healthcare, highlighting their limitations and proposing recommendations to enhance clinical utility and address privacy, bias, and communication challenges.

Contribution

It provides a comprehensive critique of current federated learning methods in healthcare and offers practical recommendations for overcoming key methodological and implementation issues.

Findings

Most current methods are unsuitable for clinical use due to flaws

Privacy, bias, and communication are major challenges in federated healthcare models

Proposed solutions aim to improve model quality and clinical applicability

Abstract

Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.