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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…
cs.DC2023
Synthesizing Proxy Applications for MPI Programs
Jiyu Luo, Tao Yan, Qingguo Xu +2
Proxy applications (proxy-apps) are basic tools for evaluating the performance of specific workloads on high-performance computing (HPC) systems. Since the development of high-fide…