most citedEvery Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction

11 citations · 13 across the 3 of their papers we have counts for

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cs.DC2023

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…

cs.DC2021★ 1 cited

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…