12 citations · 25 across the 9 of their papers we have counts for
4 papers · 1 filter
FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
Qifan Yan, Andrew Liu, Shiqi He +2
Federated learning (FL) is a machine learning paradigm that facilitates massively distributed model training with end-user data on edge devices directed by a central server. Howeve…
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…
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,…
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…