3 papers
cs.LG2025
ILoRA: Federated Learning with Low-Rank Adaptation for Heterogeneous Client Aggregation
Junchao Zhou, Junkang Liu, Fanhua Shang
Federated Learning with Low-Rank Adaptation (LoRA) faces three critical challenges under client heterogeneity: (1) Initialization-Induced Instability due to random initialization m…
cs.LG2025
DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Norm
Junkang Liu, Yuxuan Tian, Fanhua Shang +4
To prevent inference attacks in Federated Learning (FL) and reduce the leakage of sensitive information, Client-level Differentially Private Federated Learning (CL-DPFL) is widely…
cs.LG2025
FedMuon: Accelerating Federated Learning with Matrix Orthogonalization
Junkang Liu, Fanhua Shang, Junchao Zhou +3
The core bottleneck of Federated Learning (FL) lies in the communication rounds. That is, how to achieve more effective local updates is crucial for reducing communication rounds.…