no code implementations • 16 Feb 2024 • Richeng Jin, Yujie Gu, Kai Yue, Xiaofan He, Zhaoyang Zhang, Huaiyu Dai
In this paper, we propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously.
no code implementations • 10 Nov 2023 • Yifei Yang, Peng Wang, Xiaofan He, Dongmian Zou
Detecting unusual patterns in graph data is a crucial task in data mining.
no code implementations • 19 Feb 2023 • Richeng Jin, Xiaofan He, Caijun Zhong, Zhaoyang Zhang, Tony Quek, Huaiyu Dai
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks.
no code implementations • 19 Aug 2022 • Yifei Yang, Dongmian Zou, Xiaofan He
Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method.
no code implementations • 15 Apr 2020 • Richeng Jin, Xiaofan He, Huaiyu Dai
Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity.
no code implementations • 25 Feb 2020 • Richeng Jin, Yufan Huang, Xiaofan He, Huaiyu Dai, Tianfu Wu
We present Stochastic-Sign SGD which utilizes novel stochastic-sign based gradient compressors enabling the aforementioned properties in a unified framework.
no code implementations • 27 Feb 2019 • Richeng Jin, Xiaofan He, Huaiyu Dai
The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources.
no code implementations • 8 Sep 2018 • Richeng Jin, Xiaofan He, Huaiyu Dai
While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals.