30 citations · 146 across the 26 of their papers we have counts for
35 papers
FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
Junkang Liu, Fanhua Shang, Yuanyuan Liu +3
Although Federated Learning has been widely studied in recent years, there are still high overhead expenses in each communication round for large-scale models such as Vision Transf…
FedNSAM:Consistency of Local and Global Flatness for Federated Learning
Junkang Liu, Fanhua Shang, Yuxuan Tian +2
In federated learning (FL), multi-step local updates and data heterogeneity usually lead to sharper global minima, which degrades the performance of the global model. Popular FL al…
Taming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID Data
Junkang Liu, Fanhua Shang, Hongying Liu +3
Second-order optimizers can significantly accelerate large-scale training, yet their naive federated variants are often unstable or even diverge on non-IID data. We show that a key…
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…
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…
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.…