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

This research paper presents Practical Federated Unlearning (PFU), a method for removing a specific client's data contribution from a machine learning model in federated learning (a system where multiple computers train a shared model without sending raw data to a central location). PFU improves on existing approaches by requiring only one round of communication and avoiding the need to store historical model updates or wait for all clients to participate. The method uses the Fisher Information Matrix (a mathematical tool that identifies which parts of the model are most affected by specific data) to find and adjust the most sensitive model parameters related to that client's data.