25 citations · 63 across the 37 of their papers we have counts for
6 papers · 1 filter
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation
Rui Ye, Yaxin Du, Zhenyang Ni +2
In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data hetero…
Federated Learning Empowered by Generative Content
Rui Ye, Xinyu Zhu, Jingyi Chai +2
Federated learning (FL) enables leveraging distributed private data for model training in a privacy-preserving way. However, data heterogeneity significantly limits the performance…
Malicious Agent Detection for Robust Multi-Agent Collaborative Perception
Yangheng Zhao, Zhen Xiang, Sheng Yin +3
Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. Ho…
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
Chenxin Xu, Robby T. Tan, Yuhong Tan +3
Exploring spatial-temporal dependencies from observed motions is one of the core challenges of human motion prediction. Previous methods mainly focus on dedicated network structure…
Joint-Relation Transformer for Multi-Person Motion Prediction
Qingyao Xu, Weibo Mao, Jingze Gong +5
Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people. Transformer-based meth…
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
Rui Ye, Mingkai Xu, Jianyu Wang +3
This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of…