FORGET ME: Federated Unlearning for Face Generation Models

Federated face generation technology leverages decentralized private data to achieve high-quality face synthesis. However, regulations such as the GDPR confer users the right to be forgotten, necessitating the removal of contributions from specific clients in the global model. Existing generation model unlearning methods are primarily designed for centralized environments and are inadequate for addressing the constraints of data privacy storage and limited client computational resources in federated settings. To address this gap, we propose F2GU, the first federated unlearning framework specifically tailored for face generation models, enabling the effective removal of contributions associated with specific clients (identities) while ensuring privacy. Our proposed Generation Trajectory Redirection method dynamically guides the generation trajectory away from target identities, thereby effectively eliminating contributions from specific clients. Additionally, we devise a Mirroring-guided Trajectory Optimization strategy that constructs a mirror projection utilizing the retained client trajectory origins to ensure the generative capabilities of the model are preserved post-unlearning. We conduct extensive experiments on two mainstream face generation models (GAN and Diffusion Model) across three different datasets.The results indicate that our method demonstrates superior performance in both the success rate of identity unlearning and the preservation of generation quality. The code can be available at https://github.com/FanQi-AI/FFGU.