12 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
Federated Learning Hyper-Parameter Tuning from a System Perspective
Huanle Zhang, Lei Fu, Mi Zhang +4
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-…
cs.LG2022★ 12 cited
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
Lei Fu, Huanle Zhang, Ge Gao +2
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia. In a typical…
cs.LG2021
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective
Huanle Zhang, Mi Zhang, Xin Liu +2
Federated learning (FL) hyper-parameters significantly affect the training overheads in terms of computation time, transmission time, computation load, and transmission load. Howev…