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arXiv · 2412.05894

Batch effects can impair federated learning in multi-center omics studies

Yuliya Burankova·Julian Klemm·Jens J. G. Lohmann·Anne Hartebrodt·Ahmad Taheri·Niklas Probul·Jan Baumbach·Olga Zolotareva
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Abstract

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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Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann, Anne Hartebrodt, Ahmad Taheri, Niklas Probul, Jan Baumbach, Olga Zolotareva. 2024-12-08. Batch effects can impair federated learning in multi-center omics studies. https://arxiv.org/abs/2412.05894

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