Federated Learning: Attacks, Defenses, Opportunities, and Challenges

arXiv:2403.06067v1 Announce Type: new
Abstract: Using dispersed data and training, federated learning (FL) moves AI capabilities to edge devices or does tasks locally. Many consider FL the start of a new era in AI, yet it is still immature. FL has not garnered the community's trust since its security and privacy implications are controversial. FL's security and privacy concerns must be discovered, analyzed, and recorded before widespread usage and adoption. A solid comprehension of risk variables allows an FL practitioner …

ai capabilities arxiv attacks capabilities challenges community cs.cr data defenses devices edge edge devices federated federated learning locally opportunities privacy security start training trust