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cs.LG2026
HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices
Qiyuan Chen, Xian Wu, Yanan Ma +1
Federated learning (FL) on memory-constrained edge devices faces a dilemma: first-order (FO) optimization (i.e., backpropagation) demands substantial memory, whereas zeroth-order (…
cs.LG2026
HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
Zihan Fang, Zheng Lin, Senkang Hu +5
While federated learning (FL) enables fine-tuning of large language models (LLMs) without compromising data privacy, the substantial size of an LLM renders on-device training impra…
cs.LG2025
Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
Senkang Hu, Yanan Ma, Yihang Tao +5
Large language models (LLMs) have achieved remarkable success in various tasks, such as decision-making, reasoning, and question answering. They have been widely used in edge devic…