Federated Learning Privacy: Attacks, Defenses, Applications, and Policy Landscape - A Survey

arXiv:2405.03636v1 Announce Type: new
Abstract: Deep learning has shown incredible potential across a vast array of tasks and accompanying this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology enabling collaborative training of machine learning models without the need …

applications array arxiv attacks cs.cr cs.lg data deep learning defenses devices federated federated learning growth large personal personal devices policy privacy survey vast