3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
Zihao Zhao, Yuzhu Mao, Zhenpeng Shi +4
The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typica…
cs.DC2022★ 3 cited
Towards Efficient Communications in Federated Learning: A Contemporary Survey
Zihao Zhao, Yuzhu Mao, Yang Liu +4
In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privac…
cs.DC2022
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
Yuzhu Mao, Zihao Zhao, Meilin Yang +5
Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency…