Robust Federated Unlearning

Federated unlearning (FU) algorithms offer participants in federated learning (FL) the "right to be forgotten'' for their individual data and its impact on a collaboratively trained model. Existing FU algorithms primarily focus on accelerating the retraining process and enhancing the utility of the retrained models following data removal requests. However, these approaches generally lack consideration for the robustness of FU algorithms in potential adversarial environments, where adversaries can craft malicious data removal requests to compromise the retrained model. In this work, we introduce a robust federated unlearning framework (robustFU) which notably enhances the resilience of FU algorithms against a wide range of adversarial attacks. In robustFU, we design a novel dynamic conflict sample compensation algorithm that dynamically reintroduces randomly generated samples with significant information gain to the participating clients during retraining. Additionally, robustFU employs an innovative global reweighting mechanism which adjusts the weight of each model update during the global aggregation, based on its degree of misalignment with the trained model prior to unlearning. Extensive experiments demonstrates the effectiveness and robustness of the proposed robustFU framework under adversarial environments. Furthermore, robustFU significantly accelerates the retraining process, achieving a 2.53× speed-up compared to the retrain from the scratch baseline.