25 citations · 91 across the 22 of their papers we have counts for
8 papers · 1 filter
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Sikai Bai, Shuaicheng Li, Weiming Zhuang +7
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fracti…
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning
Xueyang Tang, Song Guo, Jie Zhang
Recently, data heterogeneity among the training datasets on the local clients (a.k.a., Non-IID data) has attracted intense interest in Federated Learning (FL), and many personalize…
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning
Yi Liu, Song Guo, Jie Zhang +3
To eliminate the requirement of fully-labeled data for supervised model training in traditional Federated Learning (FL), extensive attention has been paid to the application of Sel…
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…
Personalized Federated Learning with Contextualized Generalization
Xueyang Tang, Song Guo, Jingcai Guo
The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the trainin…
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases
Huawei Huang, Kangying Lin, Song Guo +2
Although the challenge of the device connection is much relieved in 5G networks, the training latency is still an obstacle preventing Federated Learning (FL) from being largely ado…
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…
Position-Aware Convolutional Networks for Traffic Prediction
Shiheng Ma, Jingcai Guo, Song Guo +1
Forecasting the future traffic flow distribution in an area is an important issue for traffic management in an intelligent transportation system. The key challenge of traffic predi…