1 citations · 1 across the 4 of their papers we have counts for
4 papers
Ordered Local Momentum for Asynchronous Distributed Learning under Arbitrary Delays
Chang-Wei Shi, Shi-Shang Wang, Wu-Jun Li
Momentum SGD (MSGD) serves as a foundational optimizer in training deep models due to momentum's key role in accelerating convergence and enhancing generalization. Meanwhile, async…
Ordered Momentum for Asynchronous SGD
Chang-Wei Shi, Yi-Rui Yang, Wu-Jun Li
Distributed learning is essential for training large-scale deep models. Asynchronous SGD (ASGD) and its variants are commonly used distributed learning methods, particularly in sce…
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning
Kun Wang, Yi-Rui Yang, Wu-Jun Li
Asynchronous federated learning (AFL) is an effective method to address the challenge of device heterogeneity in cross-device federated learning. However, AFL is usually incompatib…
Global Momentum Compression for Sparse Communication in Distributed Learning
Chang-Wei Shi, Shen-Yi Zhao, Yin-Peng Xie +2
With the rapid growth of data, distributed momentum stochastic gradient descent~(DMSGD) has been widely used in distributed learning, especially for training large-scale deep model…