2 papers
cs.LG2026
HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training
Aakriti Lnu, Zhe Li, Dandan Liang +3
Split learning (SL) enables collaborative training of large language models (LLMs) between resource-constrained edge devices and compute-rich servers by partitioning model computat…
cs.LG2026
Decoupled Split Learning via Auxiliary Loss
Anower Zihad, Felix Owino, Ming Tang +1
Split learning is a distributed training paradigm where a neural network is partitioned between clients and a server, which allows data to remain at the client while only intermedi…