3 papers
cs.DC2025
Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
Dandan Liang, Jianing Zhang, Evan Chen +3
Split Federated Learning (SFL) enables scalable training on edge devices by combining the parallelism of Federated Learning (FL) with the computational offloading of Split Learning…
cs.LG2025
Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization
Zhe Li, Bicheng Ying, Zidong Liu +2
Zeroth-order (ZO) optimization enables dimension-free communication in federated learning (FL), making it attractive for fine-tuning of large language models (LLMs) due to signific…
cs.LG2025
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
Bicheng Ying, Zhe Li, Haibo Yang
This work tackles the fundamental challenges in Federated Learning (FL) posed by arbitrary client participation and data heterogeneity, prevalent characteristics in practical FL se…