Privacy-Preserving Federated Unlearning with Certified Client Removal

arXiv:2404.09724v1 Announce Type: new
Abstract: In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client's influence from the global model in Federated Learning (FL) systems, thereby ensuring the ``right to be forgotten" (RTBF). State-of-the-art methods for unlearning use historical data from FL clients, such as gradients or locally trained models. However, studies have revealed significant information leakage in this setting, with the possibility of reconstructing a user's local data from their uploaded information. Addressing …

art arxiv attention certified client clients cs.cr data federated federated learning global influence privacy right to be forgotten state systems