Survival Model Optimization via Federated Learning: A Study Combining Simulations and Experiments

Federated Learning is an emerging, powerful approach that allows training an artificial intelligence model in distributed setting. Two survival models, Cox and DeepSurv, have been trained in a federated setting, exploiting both code simulations and real experiments on the new platform, developed by the GenoMed4All consortium. Different scenarios have been tested by splitting a Myelodysplastic Syndrome dataset into three nodes and performing feature removal. A significant gain in model performance has been observed due to federated aggregation.

Recommended citation: Casadei, F., Carota, L., Asti, G., D’Amico, S., Piscia, D., Zazo, S., ... & Giampieri, E. (2024, December). Survival Model Optimization via Federated Learning: A Study Combining Simulations and Experiments. In 2024 IEEE International Conference on Big Data (BigData) (pp. 7658-7667). IEEE. /files/2025-01-16-survivalmodeloptmitization.pdf