Network scaling in swarm learning across real hospitals of different sizes: a KPI-driven analysis on the eICU Collaborative Research Database

Background. Distributed learning lets hospitals train prediction models jointly without exchanging patient records, but how the benefits of a swarm network change as it grows, and which hospitals gain most, have so far been studied with artificial partitions of single-center databases. We examined network scaling on real hospitals that differ in size, volume and sepsis prevalence. Methods. We applied the seven-KPI swarm-learning evaluation framework (Swarm Gain, Local Gain, Relative Local Gain, Model Stability Index, Jain's index, Data Utility Index and label entropy) to progressive network sc

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