Federated Unlearning with Gradient Adaptive Shaping

Federated unlearning aims to efficiently remove the influence of a specific client from a trained global model in federated learning without full retraining. However, existing approaches struggle to preserve the utility of the remaining clients because unlearning can be unstable at both the client and server stages. Local forgetting updates may become overly aggressive and destabilize client side optimization, while server aggregation can amplify conflicts between forgetting updates and retained client updates. To address both failure modes within a unified framework, we propose Federated Unlearning with Gradient Adaptive Shaping (FUGAS), a history-free gradient shaping framework that stabilizes the entire unlearning pipeline. On the unlearning client side, we employ a bounded preference objective that utilizes the pre-unlearning model predictions on the forgetting data as negative references to controllably steer the model away from unlearning client knowledge while requiring only minimal storage for reference caches rather than full historical updates. On the server side, we introduce a compatibility projection mechanism that reshapes the aggregated unlearning update to remain compatible with directions estimated from retained clients. We provide a theoretical analysis indicating that FUGAS promotes stability and ensures non-increasing empirical risk on retained distributions while establishing an excess risk bound relative to retraining. Extensive experiments demonstrate that FUGAS achieves effective unlearning while consistently maintaining high accuracy on retained data.