35 citations · 40 across the 5 of their papers we have counts for
4 papers · 1 filter
PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
Yanlong Wang, Jian Xu, Fei Ma +3
Time series forecasting remains a critical challenge across various domains, often complicated by high-dimensional data and long-term dependencies. This paper presents a novel tran…
Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Jian Xu, Xinyi Tong, Shao-Lun Huang
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One…
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout
Jian Xu, Meiling Yang, Wenbo Ding +1
The label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning, which is particularly de…
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
Jian Xu, Shao-Lun Huang, Linqi Song +1
Gradient-based training in federated learning is known to be vulnerable to faulty/malicious clients, which are often modeled as Byzantine clients. To this end, previous work either…