2 citations · 8 across the 11 of their papers we have counts for
8 papers · 1 filter
Client-Centric Federated Adaptive Optimization
Jianhui Sun, Xidong Wu, Heng Huang +1
Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. With an increasing scale of clients and…
On the Role of Server Momentum in Federated Learning
Jianhui Sun, Xidong Wu, Heng Huang +1
Federated Averaging (FedAvg) is known to experience convergence issues when encountering significant clients system heterogeneity and data heterogeneity. Server momentum has been p…
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
Xidong Wu, Jianhui Sun, Zhengmian Hu +2
The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To add…
Federated Conditional Stochastic Optimization
Xidong Wu, Jianhui Sun, Zhengmian Hu +3
Conditional stochastic optimization has found applications in a wide range of machine learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the dema…
Enhance Diffusion to Improve Robust Generalization
Jianhui Sun, Sanchit Sinha, Aidong Zhang
Deep neural networks are susceptible to human imperceptible adversarial perturbations. One of the strongest defense mechanisms is \emph{Adversarial Training} (AT). In this paper, w…
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
Haohui Wang, Yuzhen Mao, Yujun Yan +8
Non-IID transfer learning on graphs is crucial in many high-stakes domains. The majority of existing works assume stationary distribution for both source and target domains. Howeve…