In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maxi?mizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings.

翻译：本文提出CPN-FedSL，一种基于算力网络（CPN）的新型灵活联邦分裂学习（FedSL）框架。我们构建专用模型捕捉基本设置与学习特性（如训练流程、时延与收敛性）。基于该模型，引入资源使用效能（RUE）这一融合训练效用与系统成本的新颖性能指标，并通过综合考量客户端准入、模型分割、服务器选择、路由与带宽分配（即混合整数分式规划），构建最大化RUE的多元调度问题。我们设计Refinery高效方法，首先对分式目标函数与非凸约束进行线性化，随后通过多轮基于贪心的舍入算法求解转化后的问题。大量评估证实，CPN-FedSL优于标准及最先进的学习框架（如FedAvg与SplitFed），且Refinery轻量高效，在多种设置下显著优于其变体及实际启发式方法。