A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data
arXiv:2509.10517v3 Announce Type: replace-cross Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use. Federated Learning (FL) is privacy-preserving, yet its behavior under non-IID and imbalanced conditions needs scrutiny. We benchmark five FL strategies - FedAvg, FedProx, FedAdagrad, FedAdam, and FedCluster - for mortality prediction on the MIMIC-IV dataset, partitioning 466,351 admissions across five car
Record details
Published: 27 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Privacy · Healthcare
Retrieved: 27 July 2026
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ethics.ai (27 July 2026), “A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data,” evidence record 13591, https://ethics.ai/record/13591 (originally published by arXiv cs.CY).
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