25 citations · 32 across the 3 of their papers we have counts for
4 papers
Parameterized Knowledge Transfer for Personalized Federated Learning
Jie Zhang, Song Guo, Xiaosong Ma +3
In recent years, personalized federated learning (pFL) has attracted increasing attention for its potential in dealing with statistical heterogeneity among clients. However, the st…
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…
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning
Haozhao Wang, Zhihao Qu, Song Guo +3
Federated Learning is a powerful machine learning paradigm to cooperatively train a global model with highly distributed data. A major bottleneck on the performance of distributed…
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…