Federated Unlearning for On-Device Recommendation

Abstract

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.