Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics

arXiv:2401.05146v2 Announce Type: replace-cross
Abstract: Federated Learning (FL) enables collaborative training of a Machine Learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by keeping data stored locally. Instead of centralizing raw data, FL exchanges locally refined model parameters to build a global model incrementally. While FL is more compliant with emerging regulations such as the European General Data Protection Regulation (GDPR), ensuring the right to be forgotten in this context - allowing FL participants …

arxiv build centralizing cs.cr cs.lg data design evaluation exchanges federated federated learning global guidelines institutions locally machine machine learning metrics preservation privacy survey training