Fast FedUL

Fast FedUL represents a class of methods focused on accelerating federated unlearning, a crucial aspect of federated learning that addresses data privacy concerns and malicious attacks by efficiently removing specific data from a trained model. Current research emphasizes developing training-free algorithms, like Fast-FedUL, that avoid computationally expensive retraining, often achieving significant speed improvements (e.g., 1000x faster) while maintaining high accuracy. This area is significant because it enhances the practicality and scalability of federated learning, enabling more robust and privacy-preserving machine learning applications.