When Federated Learning Meets Machine Unlearning

This research paper explores the intersection of Incremental Learning and Unlearning in the context of machine learning, with a particular emphasis on dynamic environments where data evolves rapidly, and models must continuously adapt. Incremental learning allows machine learning models to efficiently update their knowledge without retraining from scratch, while incremental unlearning ensures compliance with privacy regulations or user requests by selectively removing specific data points from the model. The paper discusses several key techniques for balancing learning and unlearning, including Elastic Weight Consolidation (EWC), gradient-based unlearning, fine-tuning, and memory-based methods.