Poisoning Federated Recommender Systems with Fake Users
Ming Yin, Yichang Xu, Minghong Fang, and Neil Zhenqiang Gong

TL;DR
This paper presents PoisonFRS, a novel attack method that promotes targeted items in federated recommender systems without needing extra data, outperforming existing methods and making detection difficult.
Contribution
PoisonFRS is the first attack that promotes items without requiring knowledge of user data or server aggregation rules, enhancing attack effectiveness and stealth.
Findings
PoisonFRS effectively promotes targeted items in real-world datasets.
It outperforms benchmark attacks relying on additional information.
Fake user updates are indistinguishable from genuine ones in latent space.
Abstract
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. Current poisoning attacks on federated recommender systems often rely on additional information, such as the local training data of genuine users or item popularity. However, such information is challenging for the potential attacker to obtain. Thus, there is a need to develop an attack that requires no extra information apart from item embeddings obtained from the server. In this paper, we introduce a novel fake user…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpam and Phishing Detection · Internet Traffic Analysis and Secure E-voting · Advanced Steganography and Watermarking Techniques
