Review of Federated Learning and Its Security and Privacy Protection
Federated Learning(FL)is a new distributed machine earning technology that only requires local maintenance of data and can train a common model through the cooperation of all parties,which mitigates issues pertaining to data collection and privacy security in conventional machine learning.However,with the application and development of FL,it is still exposed to various attacks.To ensure the security of FL,the attack mode in FL and the corresponding privacy protection technology must be investigated.Herein,first,the background knowledge and relevant definitions of FL are introduced,and the development process and classification of FL are summarized.Second,the security three elements of FL are expounded,and the security issues and research progress of FL are summarized from two perspectives based on security sources and the security three elements.Subsequently,privacy protection technologies are classified.This paper summarizes four common privacy protection technologies used in FL:Secure Multiparty Computing(SMC),Homomorphic Encryption(HE),Differential Privacy(DP),and Trusted Execution Environment(TEE).Finally,the future research direction for FL is discussed.
Federated Learning(FL)data securityattack modeprivacy protectionsecurity three elements
国家科技期刊平台
NSTL
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