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

2024-09-15

arXiv.org

AI Summary

Federated learning (FL) faces critical clinical deployment barriers in healthcare—including privacy leakage, poor generalizability, and excessive communication overhead—limiting its real-world utility. This study presents a systematic review of medical FL practices published before May 2024 and introduces, for the first time, a multi-dimensional evaluation framework tailored to clinical applicability. We propose a tiered recommendation system that jointly addresses privacy preservation (via differential privacy), robustness (through robust aggregation and heterogeneity-aware adaptation), and deployment efficiency (using communication compression). Furthermore, we design a scalable collaborative training paradigm. Empirical analysis reveals that over 80% of existing studies suffer from significant methodological flaws. We distill 12 actionable guidelines and identify five high-potential clinical application scenarios. Collectively, this work provides both theoretical foundations and practical pathways toward trustworthy, deployable medical FL modeling.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Evaluate clinical utility of federated learning
• Identify methodological flaws in healthcare applications
• Propose solutions to improve model development

Innovation

• Federated Learning healthcare application
• Addressing privacy generalization issues
• Improving model development quality