Poisoning Federated Recommender Systems with Fake Users
Ming Yin, Yichang Xu, Minghong Fang, Neil Zhenqiang Gong · 2024
Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attacks are particularly notable among user-side attacks, as participants upload malicious model updates to deceive the global model, often intending to promote or demote specific targeted items. This study investigates strategies for executing promotion attacks in federated recommender systems.