Privacy and Robustness in Federated Learning: Attacks and Defenses. (arXiv:2012.06337v3 [cs.CR] UPDATED)

As data are increasingly being stored in different silos and societies
becoming more aware of data privacy issues, the traditional centralized
training of artificial intelligence (AI) models is facing efficiency and
privacy challenges. Recently, federated learning (FL) has emerged as an
alternative solution and continue to thrive in this new reality. Existing FL
protocol design has been shown to be vulnerable to adversaries within or
outside of the system, compromising data privacy and system robustness. Besides
training powerful global models, …

attacks privacy