Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning

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.

Backdoor Attack Federated Learning

Robust Training of Federated Models with Extremely Label Deficiency

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.

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

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.

Federated Learning

RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search

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.

Reinforcement Learning (RL) Self-Supervised Learning

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

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.

Federated Learning Meta-Learning +3