25 citations · 32 across the 6 of their papers we have counts for
Showing 2022Show all
3 papers · 1 filter
cs.LG2022★ 2 cited
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu +3
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communicat…
cs.LG2022★ 1 cited
Anchor Sampling for Federated Learning with Partial Client Participation
Feijie Wu, Song Guo, Zhihao Qu +3
Compared with full client participation, partial client participation is a more practical scenario in federated learning, but it may amplify some challenges in federated learning,…
cs.LG2022★ 3 cited
Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression
Feijie Wu, Shiqi He, Song Guo +4
Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive…