3 citations · 5 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning
Guangfeng Yan, Tan Li, Tian Lan +2
Next-generation wireless networks, such as edge intelligence and wireless distributed learning, face two critical challenges: communication efficiency and privacy protection. In th…
Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning
Guangfeng Yan, Tan Li, Kui Wu +1
Communication efficiency and privacy protection are two critical issues in distributed machine learning. Existing methods tackle these two issues separately and may have a high imp…
Adaptive Top-K in SGD for Communication-Efficient Distributed Learning
Mengzhe Ruan, Guangfeng Yan, Yuanzhang Xiao +2
Distributed stochastic gradient descent (SGD) with gradient compression has become a popular communication-efficient solution for accelerating distributed learning. One commonly us…
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning
Guangfeng Yan, Shao-Lun Huang, Tian Lan +1
Gradient quantization is an emerging technique in reducing communication costs in distributed learning. Existing gradient quantization algorithms often rely on engineering heuristi…
Communication Efficient Federated Learning with Adaptive Quantization
Yuzhu Mao, Zihao Zhao, Guangfeng Yan +4
Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy preserving measures and great potentials in some distributed but privacy-sensitive ap…