Forgetting without Seeing: Blind Federated Unlearning with Pure Random Noise

Federated unlearning aims to remove the influence of a designated client from a trained global model while preserving model utility on the retained clients. Existing methods presuppose a fixed degree of unlearning client participation, whereas client availability is unpredictable in practice. Therefore, we formalize this setting as blind federated unlearning, where the unlearning client may never participate or may depart at any round, and the remaining rounds are completed solely by the retained clients. We find that when the unlearning client departs after partial participation, continuing with retained clients alone can substantially reverse the unlearning already achieved, revealing the need for persistent unlearning signals after client departure. To provide persistent surrogate signals without relying on semantic information about the unlearning data or auxiliary data, we propose Noise-as-Proxy Erasure (NoPE), which uses pure random noise generated at the server as proxy data. NoPE employs distance-based loss weighting to regulate the contribution of individual noise samples, enabling fine-grained control over the unlearning process. Extensive experiments demonstrate that NoPE achieves effective unlearning while maintaining competitive performance on retained data across different blind federated unlearning scenarios, and can be attached to existing federated unlearning methods to continue unlearning after client departure.