Federated Unlearning for On-Device Recommendation

The increasing data privacy concerns in recommendation systems have made federated recommendations attract more and more attention. Existing federated recommendation systems mainly focus on how to effectively and securely learn personal interests and preferences from their on-device interaction data. Still, none of them considers how to efficiently erase a user's contribution to the federated training process. We argue that such a dual setting is necessary. First, from the privacy protection perspective, "the right to be forgotten (RTBF)" requires that users have the right to withdraw their data contributions. Without the reversible ability, federated recommendation systems risk breaking data protection regulations. On the other hand, enabling a federated recommender to forget specific users can improve its robustness and resistance to malicious clients' attacks.

To support user unlearning in federated recommendation systems, we propose an efficient unlearning method FRU (Federated Recommendation Unlearning), inspired by the log-based rollback mechanism of transactions in database management systems. It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction. However, storing all historical parameter updates on resource-constrained personal devices is challenging and even infeasible. In light of this challenge, we propose a small-sized negative sampling method to reduce the number of item embedding updates and an importance-based update selection mechanism to store only important model updates. To evaluate the effectiveness of FRU, we propose an attack method to disturb federated recommenders via a group of compromised users. Then, we use FRU to recover recommenders by eliminating these users' influence. Finally, we conduct extensive experiments on two real-world recommendation datasets (i.e. MovieLens-100k and Steam-200k) with two widely used federated recommenders to show the efficiency and effectiveness of our proposed approaches.