40 citations · 41 across the 9 of their papers we have counts for
5 papers · 1 filter
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning
Kangkang Sun, Xiaojin Zhang, Xi Lin +3
Federated Learning (FL) is a novel privacy-protection distributed machine learning paradigm that guarantees user privacy and prevents the risk of data leakage due to the advantage…
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
Xing Ai, Jialong Zhou, Yulin Zhu +4
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. Howev…
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats
Gaolei Li, Yuanyuan Zhao, Wenqi Wei +1
Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimension…
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios
Haochen Mei, Gaolei Li, Jun Wu +1
Federated learning (FL) naturally faces the problem of data heterogeneity in real-world scenarios, but this is often overlooked by studies on FL security and privacy. On the one ha…
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications
Gaolei Li, Yuanyuan Zhao, Yi Li
Federated learning (FL) provides an emerging approach for collaboratively training semantic encoder/decoder models of semantic communication systems, without private user data leav…