Split Learning Over Wireless Networks: Parallel Design and Resource Management

Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer. The existing SL approach conducts the training process sequentially across devices, which incurs significant training latency especially when the number of devices is large. In this paper, we design a novel SL scheme to reduce the training latency, named C luster-based P arallel SL (CPSL) which conducts model training in a “first-parallel-then-sequential” manner. Specifically, the CPSL is to partition devices into several clusters, parallelly train device-side models in each…

• PC
  Peng Cheng Laboratory

  Award: PCL2021A09-B2
• NN
  National Natural Science Foundation of China

  Award: 6220012314
• HT
  Huawei Technologies
• NS
  Natural Sciences and Engineering Research Council of Canada