FedRecTID: A General Robust Federated Recommendation Framework Based on Target Items Detection

Federated recommender systems (FedRecs) have received a lot of attention in recent years because of their excellent ability to protect privacy. But recent research has shown that federated recommender systems are vulnerable to poisoning attacks. Attackers can pollute training data or local models to influence the recommendation rankings of target items. To reduce the impact of poisoning attacks on federated recommender systems, we propose a robust federated recommendation framework based on target item detection called FedRecTID, which can effectively resist the impact of targeted attacks in federated recommender systems. FedRecTID can be divided into three steps, including the identification of suspicious items, vote for validation and removal of the effects of attack. Specifically, after receiving the gradients uploaded by the clients, the server calculates the suspicious scores of items by calculating the cosine similarity of the item embedding in the last two rounds. Based on the scores, the suspicious items can be determined by statistical anomaly detection methods. Then these items are sent to the selected users for verification. After receiving users’ vote, the server validates it based on the majority of the voting mechanism, and filters out the target item without updating it. Experimental results on two real datasets demonstrate that our proposed framework for FedRecs can effectively defend existing targeted attacks against FedRecs. And FedRecTID does not harm the recommendation performance.