most citedFedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data

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cs.LG2023★ 1 cited

FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data

Hao Sun, Li Shen, Shixiang Chen +4

Federated learning is an emerging distributed machine learning method, enables a large number of clients to train a model without exchanging their local data. The time cost of comm…

cs.LG2023

AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks

Hao Sun, Li Shen, Qihuang Zhong +6

Sharpness aware minimization (SAM) optimizer has been extensively explored as it can generalize better for training deep neural networks via introducing extra perturbation steps to…