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cs.LG2026
ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling
Wentao Dai, Xuanran Li, Yuxiang Zhang +2
Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy. Although existing studies proposed pipeline parallelism to a…
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…
cs.LG2023
Price of Stability in Quality-Aware Federated Learning
Yizhou Yan, Xinyu Tang, Chao Huang +1
Federated Learning (FL) is a distributed machine learning scheme that enables clients to train a shared global model without exchanging local data. The presence of label noise can…