1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CR2024
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Yichang Xu, Ming Yin, Minghong Fang +1
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as cl…
cs.CR2024★ 1 cited
Poisoning Federated Recommender Systems with Fake Users
Ming Yin, Yichang Xu, Minghong Fang +1
Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attac…
cs.LG2023
Toward Robust Recommendation via Real-time Vicinal Defense
Yichang Xu, Chenwang Wu, Defu Lian
Recommender systems have been shown to be vulnerable to poisoning attacks, where malicious data is injected into the dataset to cause the recommender system to provide biased recom…