3 citations · 10 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits
Tan Li, Linqi Song
Communication bottleneck and data privacy are two critical concerns in federated multi-armed bandit (MAB) problems, such as situations in decision-making and recommendations of con…
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
Jian Xu, Shao-Lun Huang, Linqi Song +1
Gradient-based training in federated learning is known to be vulnerable to faulty/malicious clients, which are often modeled as Byzantine clients. To this end, previous work either…
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…