3 papers
cs.CR2025
Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
Xian Qin, Xue Yang, Xiaohu Tang
Federated Learning (FL) allows collaborative model training across distributed clients without sharing raw data, thus preserving privacy. However, the system remains vulnerable to…
cs.LG2024
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning
Xue Yang, Depan Peng, Yan Feng +3
Bidirectional privacy-preservation federated learning is crucial as both local gradients and the global model may leak privacy. However, only a few works attempt to achieve it, and…
cs.CY2024
Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings
Junbiao Pang, Muhammad Ayub Sabir, Zhuyun Wang +4
In our urban life, Illegal Driver Substitution (IDS) activity for a taxi is a grave unlawful activity in the taxi industry, possibly causing severe traffic accidents and painful so…