3 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
On the Optimal Batch Size for Byzantine-Robust Distributed Learning
Yi-Rui Yang, Chang-Wei Shi, Wu-Jun Li
Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot…
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning
Yi-Rui Yang, Kun Wang, Wu-Jun Li
Federated learning (FL) has recently become a hot research topic, in which Byzantine robustness, communication efficiency and privacy preservation are three important aspects. Howe…
Buffered Asynchronous SGD for Byzantine Learning
Yi-Rui Yang, Wu-Jun Li
Distributed learning has become a hot research topic due to its wide application in clusterbased large-scale learning, federated learning, edge computing and so on. Most traditiona…