Concurrent Split Learning Through Stable Client Clustering
Training with a fixed global batch limits how many distributed clients can provide examples in any one step. We examine a way to use additional server workers without increasing the batch processed by an individual workload. Global Clustered Parallel Split Learning (GCPSL) assigns clients to fixed clusters, executes a Parallel Split Learning with Global Sampling (GPSL) workload for each cluster concurrently, and periodically fuses the client and server model segments. In simulations with 256 logical clients, dividing the population across more workloads improves direct data participation, while smaller clusters can incur an accuracy cost. A four-H100 implementation of label-aware GCPSL re...
| Authors | Mohammad Kohankhaki, Valentin Rentschler, Anke Schmeink |
|---|---|
| Primary category | cs.DC |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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