计算机工程2024，Vol.50Issue(5)：1-15,15.DOI:10.19678/j.issn.1000-3428.0067782
联邦学习及其安全与隐私保护研究综述
Review of Federated Learning and Its Security and Privacy Protection
摘要
Abstract
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.关键词
联邦学习/数据安全/攻击方式/隐私保护/安全三要素Key words
Federated Learning(FL)/data security/attack mode/privacy protection/security three elements分类
信息技术与安全科学引用本文复制引用
熊世强,何道敬,王振东,杜润萌..联邦学习及其安全与隐私保护研究综述[J].计算机工程,2024,50(5):1-15,15.基金项目
国家自然科学基金(62062037) （62062037）
江西省自然科学基金(20212BAB202014). （20212BAB202014）
