批量效应会干扰多中心组学联邦学习，需隐私保护校正。
Batch effects can impair federated learning in multi-center omics studies
- 用联邦k均值与随机森林评估批量效应影响
- 未校正批量效应使无监督学习失效，监督学习性能下降30%以上
- 提出fedRBE实现隐私保护批量效应校正，支持缺失值和异构数据
联邦学习（FL）可在不共享患者级数据的前提下协作分析生物医学数据，但在多中心研究中可能受批量效应影响，掩盖真实生物信号。本文使用四个多中心组学数据集（转录组、蛋白质组、代谢组）及两种代表性算法（联邦k均值聚类和联邦随机森林分类），系统评估未校正批量效应的影响。结果表明，未校正的批量效应会破坏无监督联邦学习，显著降低监督学习性能。为在分布式群体组学数据中实现隐私保护的批量效应校正，我们提出fedRBE（https://featurecloud.ai/app/fedrbe），基于limma的removeBatchEffect()方法，结合安全多方计算，适用于含缺失值及特征集不一致的场景，包括蛋白质组与代谢组数据。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative analysis of biomedical data without exchanging sensitive patient-level information, but its performance in multi-center studies may be compromised by batch effects which can obscure biological signals. Here, we systematically assess the impact of uncorrected batch effects on FL outcomes using four multi-center omics datasets, including transcriptomic, proteomic, and metabolomic data, and two representative algorithms: federated k-means clustering and federated random forest classification. Our results demonstrate that uncorrected batch effects undermine unsupervised FL and can substantially degrade supervised FL performance, indicating that privacy-aware batch-effect correction is essential for reliable FL. To enable privacy-preserving BEC in distributed bulk omics data, we introduce fedRBE ( https://featurecloud.ai/app/fedrbe ), a federated implementation of limma's removeBatchEffect() method enhanced by secure multi-party computation, suitable for datasets with missing values and non-identical feature sets across clients, including proteomics and metabolomics data.
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