FedRUn: Efficient Client-Side Federated Unlearning with Rényi Entropy

With the rise of privacy regulations, machine learning models should be able to remove the influence of designated data from the trained model upon request. In Federated Learning (FL), this task becomes more challenging because the training data remain distributed across multiple clients while only model updates are shared with the server. Retraining without target data provides a natural reference for unlearning, but requires substantial distributed computation and communication costs. Existing federated unlearning (FU) methods may help to reduce this cost, but most of them rely on stored training history, previous global models, or recovery procedures involving non-requesting clients, which incurs excessive runtime, massive historical storage, repeated communications, and intensive computation overhead. To address these limitations, we propose FedRUn, an efficient client-side FU framework based on Rényi entropy maximization. FedRUn performs unlearning directly from the final trained global model without requiring stored historical model updates or repeated communication and computation costs, reducing the system overhead and making unlearning very efficient. The unlearning process is performed locally at the target client, eliminating the need for the server to store historical client model updates while allowing each client to keep its data locally. Since the target client has direct access to its local data, FedRUn can apply the Rényi-entropy objective directly to either a subset of samples or the entire local dataset. Specifically, FedRUn maximizes predictive Rényi entropy on the forget set to reduce predictive confidence, while retaining and proximal terms preserve non-forgotten samples and limit model changes. Only the resulting client-computed unlearning update is sent to the server to update the global model. This design enables client-level, sample-level, and multi-client unlearning within the same framework while reducing computation, communication, and storage requirements.