Joint client–server selection and resource allocation based on split federated learning in Edge-to-Cloud computing environments

doi:10.1016/j.comnet.2025.111502

Joint client–server selection and resource allocation based on split federated learning in Edge-to-Cloud computing environments

2025-07-05

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DOI：10.1016/j.comnet.2025.111502

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• Propose novel U-shaped split federated learning training architecture. • Joint optimization of client–server combinations and resource allocation. • Propose an improved PPO algorithm (MSHPPO) to accomplish the problem. • Show U-SFL and MSHPPO outperform FL and RL through simulation results.

Joint client–server selection and resource allocation based on split federated learning in Edge-to-Cloud computing environments

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