1 code implementation • 26 Apr 2024 • Tao Liu, Yuhang Zhang, Zhu Feng, Zhiqin Yang, Chen Xu, Dapeng Man, Wu Yang
Trained backdoored global model is more resilient to benign updates, leading to a higher attack success rate on the test set.
2 code implementations • 22 Feb 2024 • Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han
Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency.
1 code implementation • NeurIPS 2023 • Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han
Comprehensive experiments demonstrate the efficacy of FedFed in promoting model performance.
1 code implementation • 14 Jun 2022 • Yingguang Yang, Renyu Yang, Yangyang Li, Kai Cui, Zhiqin Yang, Yue Wang, Jie Xu, Haiyong Xie
More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task.
no code implementations • 25 Feb 2021 • Shaoxiong Ji, Yue Tan, Teemu Saravirta, Zhiqin Yang, Yixin Liu, Lauri Vasankari, Shirui Pan, Guodong Long, Anwar Walid
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.