1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2024
Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Models
Ziyao Wang, Bowei Tian, Yexiao He +6
The recent advancement of foundation models (FMs) has increased the demand for fine-tuning these models on large-scale cross-domain datasets. To address this, federated fine-tuning…
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…