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. 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. O
The proliferation of federated learning in biomedical research makes understanding its limitations, especially concerning data heterogeneity, critically important at this stage of its adoption.
This highlights a significant technical challenge for the robust and reliable application of AI in healthcare, particularly for sensitive multi-center omics studies.
The findings suggest that federated learning models require advanced pre-processing or more sophisticated algorithms to effectively handle batch effects, moving beyond simple data aggregation.
- · AI algorithm developers (batch effect correction)
- · Healthcare data standardization initiatives
- · Uncorrected federated learning platforms
- · Early adopters of unvalidated FL in healthcare
Federated learning applications in medicine will need to incorporate robust batch effect correction methods to ensure validity.
Increased demand for specialized AI/ML researchers skilled in handling heterogeneous biomedical datasets and privacy-preserving techniques.
This could accelerate the development of new privacy-preserving data harmonization techniques alongside federated learning, creating more complex but reliable biomedical AI ecosystems.
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Read at arXiv cs.LG