10 citations · 18 across the 6 of their papers we have counts for
6 papers
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…
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization
Feijie Wu, Song Guo, Haozhao Wang +4
In the setting of federated optimization, where a global model is aggregated periodically, step asynchronism occurs when participants conduct model training by efficiently utilizin…
On the Convergence of Quantized Parallel Restarted SGD for Central Server Free Distributed Training
Feijie Wu, Shiqi He, Yutong Yang +4
Communication is a crucial phase in the context of distributed training. Because parameter server (PS) frequently experiences network congestion, recent studies have found that tra…
Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training
Chengjie Li, Ruixuan Li, Haozhao Wang +4
Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models. As data are dist…
Heterogeneity-aware Gradient Coding for Straggler Tolerance
Haozhao Wang, Song Guo, Bin Tang +2
Gradient descent algorithms are widely used in machine learning. In order to deal with huge volume of data, we consider the implementation of gradient descent algorithms in a distr…