SoK: Challenges and Opportunities in Federated Unlearning

arXiv:2403.02437v1 Announce Type: new
Abstract: Federated learning (FL), introduced in 2017, facilitates collaborative learning between non-trusting parties with no need for the parties to explicitly share their data among themselves. This allows training models on user data while respecting privacy regulations such as GDPR and CPRA. However, emerging privacy requirements may mandate model owners to be able to \emph{forget} some learned data, e.g., when requested by data owners or law enforcement. This has given birth to an active field of …

abstract arxiv challenges collaborative cs.ai cs.dc cs.lg data federated learning gdpr opportunities parties privacy regulations requirements training training models type unlearning user data