3 citations · 5 across the 8 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning
Guangfeng Yan, Tan Li, Yuanzhang Xiao +2
Gradient compression has surfaced as a key technique to address the challenge of communication efficiency in distributed learning. In distributed deep learning, however, it is obse…
cs.LG2024
Truncated Non-Uniform Quantization for Distributed SGD
Guangfeng Yan, Tan Li, Yuanzhang Xiao +2
To address the communication bottleneck challenge in distributed learning, our work introduces a novel two-stage quantization strategy designed to enhance the communication efficie…
cs.CR2024★ 1 cited
Towards Quantum-Safe Federated Learning via Homomorphic Encryption: Learning with Gradients
Guangfeng Yan, Shanxiang Lyu, Hanxu Hou +2
This paper introduces a privacy-preserving distributed learning framework via private-key homomorphic encryption. Thanks to the randomness of the quantization of gradients, our lea…