The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining

Understand

In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm named \textit{machine unlearning}, which enables data holders to proactively erase their data from a trained model.

• Existing machine unlearning techniques focus on centralized training, where access to all holders' training data is a must for the server to conduct the unlearning process.
• It remains largely underexplored about how to achieve unlearning when full access to all training data becomes unavailable.
• One noteworthy example is Federated Learning (FL), where each participating data holder trains locally, without sharing their training data to the central server.