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cs.LG2025
Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
Baris Askin, Holger R. Roth, Zhenyu Sun +3
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its scalability is limited by synchronization overhead. Asynch…
cs.LG2024
Debiasing Federated Learning with Correlated Client Participation
Zhenyu Sun, Ziyang Zhang, Zheng Xu +3
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averagin…
cs.LG2024★ 1 cited
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
Yae Jee Cho, Luyang Liu, Zheng Xu +2
Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs wi…