指出医疗联邦学习的三大陷阱并提出改进方向
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
- 梳理2024年5月前医疗联邦学习研究，识别方法缺陷
- 发现多数方法因隐私、泛化差、通信成本高无法临床应用
- 提出可落地的改进建议，助力高质量模型开发
联邦学习在保护数据隐私的前提下，有望推动多中心医疗研究与协作。尽管近年大量研究采用或建议使用联邦学习方法，但其临床实用性仍不明确。本文综述截至2024年5月的相关研究，发现绝大多数方法存在方法学缺陷和潜在偏见，包括隐私风险、泛化能力不足及通信开销过高等问题，严重影响其在医疗场景的实际效果。为此，本文提出针对性建议与可行机遇，以解决当前挑战，提升医疗联邦学习模型的开发质量。
原文摘要 · Abstract (English)
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.
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