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cs.LG2025
Improving Early Sepsis Onset Prediction Through Federated Learning
Christoph Düsing, Philipp Cimiano
Early and accurate prediction of sepsis onset remains a major challenge in intensive care, where timely detection and subsequent intervention can significantly improve patient outc…
cs.LG2025
Distribution-Controlled Client Selection to Improve Federated Learning Strategies
Christoph Düsing, Philipp Cimiano
Federated learning (FL) is a distributed learning paradigm that allows multiple clients to jointly train a shared model while maintaining data privacy. Despite its great potential…
cs.LG2025
Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments
Christoph Düsing, Philipp Cimiano
Missing data is a persistent challenge in federated learning on electronic health records, particularly when institutions collect time-series data at varying temporal granularities…