Research Progress on Security and Privacy of Federated Learning: A Survey

Federated Learning (FL) is an emerging distributed machine learning paradigm designed to resolve the conflict between data sharing and privacy. It allows each client device to train shared models locally and perform global model aggregation on cloud servers without users having to share their data. However, there are still many security risks and malicious attacks that could breach the data privacy and confidentiality in the process of local training and information interaction. This paper investigates the security and the privacy challenges faced by FL and the corresponding defense methods. First, existing works about the FL-related surveys are studied; second, the basic concepts, the algorithm principle and the scenario classification of FL are introduced; next, examples are provided to illustrate the relevant attacks and defense knowledge of FL; then, the aggressive behaviors in FL are classified from four perspectives: the poisoning attack, the inference attack, the model attack and the adversarial attack, and the sub-aggressive behaviors are also com bed out; subsequently, the defense methods are divided according to the two directions of attack behaviors and privacy-protection technologies, and the application of different defense methods is investigated. Eventually, the future research directions on both attack problems and defense strategies in FL systems are discussed.