arXiv Machine Learning July 7, 2026 By Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann, Anne Hartebrodt, Ahmad Taheri, Niklas Probul, Jan Baumbach, Olga Zolotareva

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

Read the original on arXiv Machine Learning →

arXiv:2412. 05894v2 Announce Type: replace-cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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