When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

• URL: http://arxiv.org/abs/2305.12979v1
• Date: Mon, 22 May 2023 12:36:52 GMT
• Title: When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework
• Authors: Xinjing Yuan, Lingjun Pu, Lei Jiao, Xiaofei Wang, Meijuan Yang, Jingdong Xu
• Abstract summary: CPN-FedSL is a 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., latency, flow, convergence)
• Score: 6.871107511111629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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