A review of secure federated learning: Privacy leakage threats, protection technologies, challenges and future directions

Advances in the new generation of Internet of Things (IoT) technology are propelling the growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption of artificial intelligence (AI) technologies, such as machine and deep learning, is accelerating. Traditional machine learning models rely heavily on massive amounts of data, however collecting and processing massive amounts of data generated by network-edge devices is costly and inefficient, and poses serious risks to data privacy. As a new paradigm for statistical model training in distributed edge networks, federated learning (FL) enables data to participate in federated model training without being localized. This approach can be used to solve traditional machine learning problems of low data utilization, data privacy, and information security caused by data isolation. However, the defects of the FL framework and insecure network environments cause many security and privacy leakage problems in actual application scenarios of FL. First, the concepts, classifications, and fundamental FL principles were described. Second, the mainstream privacy security issues and classification of FL were investigated. Privacy security protection techniques for FL were then identified. Finally, challenges and future research directions for the development of FL privacy security are discussed.