Practical Federated Unlearning: A Target Client-Driven Approach to Model Forgetting

Summary

Practical Federated Unlearning (PFU) removes a target client's data contribution from a federated learning global model. It needs only one round of interaction between the target client and the aggregation server during unlearning. The server uses the Fisher Information Matrix to find the parameters most sensitive to that client's data and sends their indices to the client, which estimates its contribution with a Bayesian inference-based method and prunes those parameters, avoiding data or historical updates from other clients.