{"ID":2874987,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10517","arxiv_id":"2509.10517","title":"A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data","abstract":"Machine learning models hold significant potential for predicting in-hospital mortality, yet data privacy constraints and the statistical heterogeneity of real-world clinical data often hamper their development. Federated Learning (FL) offers a privacy-preserving solution, but its performance under non-Independent and Identically Distributed (non-IID) and imbalanced conditions requires rigorous investigation. The study presents a comparative benchmark of five federated learning strategies: FedAvg, FedProx, FedAdagrad, FedAdam, and FedCluster for mortality prediction. Using the large-scale MIMIC-IV dataset, we simulate a realistic non-IID environment by partitioning data by clinical care unit. To address the inherent class imbalance of the task, the SMOTE-Tomek technique is applied to each client's local training data. Our experiments, conducted over 50 communication rounds, reveal that the regularization-based strategy, FedProx, consistently outperformed other methods, achieving the highest F1-Score of 0.8831 while maintaining stable convergence. While the baseline FedAvg was the most computationally efficient, its predictive performance was substantially lower. Our findings indicate that regularization-based FL algorithms like FedProx offer a more robust and effective solution for heterogeneous and imbalanced clinical prediction tasks than standard or server-side adaptive aggregation methods. The work provides a crucial empirical benchmark for selecting appropriate FL strategies for real-world healthcare applications.","short_abstract":"Machine learning models hold significant potential for predicting in-hospital mortality, yet data privacy constraints and the statistical heterogeneity of real-world clinical data often hamper their development. Federated Learning (FL) offers a privacy-preserving solution, but its performance under non-Independent and...","url_abs":"https://arxiv.org/abs/2509.10517","url_pdf":"https://arxiv.org/pdf/2509.10517v2","authors":"[\"Rodrigo Tertulino\"]","published":"2025-09-03T11:32:57Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CY\"]","methods":"[]","has_code":false}