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

arXiv:2405.03636v1 Announce Type: cross
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 …

abstract applications array arxiv attacks concerns cs.cr cs.lg data deep learning devices enabling federated learning growth however landscape policy privacy survey tasks type vast