A survey on security and privacy threats to federated learning
Junpeng Zhang, Mengqian Li, Shuiguang Zeng, Bin Xie, Dongmei Zhao
Abstract
Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.