When Computing Power Network Meets Distributed Machine Learning: An
  Efficient Federated Split Learning Framework
Xinjing Yuan Lingjun Pu Lei Jiao Xiaofei Wang Meijuan Yang Jingdong Xu
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.