The Right to Be Forgotten Versus the Need to Be Remembered: Efficient Personalized Federated Unlearning With Optimal Incentives

Federated Learning (FL) enables privacy-preserving collaborative training across distributed devices without direct data sharing. However, existing FL approaches faces new challenges in upholding users' right to be forgotten (RTBF), particularly in reconciling data removal guarantees with model performance preservation. Current server-centric federated unlearning (FU) approaches suffer from two critical limitations: i) inability to accommodate personalized unlearning requirements across users, and ii) lack of verifiable data erasure guarantees. We propose a novel user-centric personalized FU (PFU) framework that shifts unlearning control to data owners while maintaining global model utility, where they independently and adaptively adjust unlearning intensity parameters (e.g., distillation temperature) to meet personalized forgetting requirements through multi-task alternating training on local data and unlearning knowledge distillation. To further resolve the fundamental conflict between users' RTBF and servers' need to be remembered (NTBR), a contract-theoretic incentive mechanism is devised to compensate users for reducing their unlearning intensity, thereby enhancing model utility. We theoretically prove the optimality of the designed contracts under multi-dimensional information asymmetry, which maximize the server's utility and motivate users to truthfully select and sign the corresponding contract items that align with their privacy expectations. Extensive experiments demonstrate that our proposed scheme significantly enhances the performance of unlearned global models and improves the utilities of both the server and users compared to existing approaches.