40 citations · 97 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
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.…
FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models
Junkang Liu, Fanhua Shang, Hongying Liu +5
AdamW has become one of the most effective optimizers for training large-scale models. We have also observed its effectiveness in the context of federated learning (FL). However, d…
FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
Liu junkang, Yuanyuan Liu, Fanhua Shang +3
For federated learning (FL) algorithms such as FedSAM, their generalization capability is crucial for real-word applications. In this paper, we revisit the generalization problem i…
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
Hua Huang, Fanhua Shang, Yuanyuan Liu +1
Federated Learning (FL) has become an active and promising distributed machine learning paradigm. As a result of statistical heterogeneity, recent studies clearly show that the per…
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
Lin Kong, Wei Sun, Fanhua Shang +2
Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have p…
Boosting Gradient for White-Box Adversarial Attacks
Hongying Liu, Zhenyu Zhou, Fanhua Shang +3
Deep neural networks (DNNs) are playing key roles in various artificial intelligence applications such as image classification and object recognition. However, a growing number of…