From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare
Ming Li, Pengcheng Xu, Junjie Hu, Zeyu Tang, Guang Yang
Abstract
Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centers 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. • Evaluate recent FL technologies in healthcare, focusing on challenges and pitfalls. • Offer a taxonomic analysis of FL in healthcare across critical aspects. • Recommend strategies for improving FL and ensuring reproducibility. • Highlight trends and opportunities to enhance FL workflow.