31 citations · 33 across the 5 of their papers we have counts for
4 papers · 1 filter
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 (…
Language-Induced Priors for Domain Adaptation
Qiyuan Chen, Jiayu Zhou, Raed Al Kontar
Domain adaptation faces a fundamental paradox in the cold-start regime. When target data is scarce, statistical methods fail to distinguish relevant source domains from irrelevant…
HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
Qiyuan Chen, Xian Wu, Yi Wang +1
Fine-tuning large models on edge devices is severely hindered by the memory-intensive backpropagation (BP) in standard frameworks like federated learning and split learning. While…
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Zheng Lin, Guanqiao Qu, Qiyuan Chen +3
Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, t…