Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

作     者：Zhe Sun Jiyuan Feng Lihua Yin Zixu Zhang Ran Li Yu Hu Chongning Na 

作者机构：Cyberspace Institute of Advanced Technology(CIAT)Guangzhou UniversityGuangzhouChina School of Electrical and Data EngineeringUniversity of Technology SydneySydneyAustralia Zhejiang LabHangzhouChina 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：1867-1886页

核心收录：

学科分类：0810[工学-信息与通信工程] 07[理学] 0708[理学-地球物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by the National Key R&D Program of China(No.2018YFB2100400) in part by the National Natural Science Foundation of China(No.62002077,61872100) in part by the China Postdoctoral Science Foundation(No.2020M682657) in part by Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110385) in part by Zhejiang Lab(No.2020NF0AB01),in part by Guangzhou Science and Technology Plan Project(202102010440). 

主　　题：Decentralized function encryption incentive mechanism differential privacy federated learning 

摘      要：Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by gradient inversion,inference attacks,etc.With a lightweight encryption overhead,function encryption is a viable secure aggregation technique in federation learning,which is often used in combination with differential privacy.The function encryption in federal learning still has the following problems:a)Traditional function encryption usually requires a trust third party(TTP)to assign the keys.If a TTP colludes with a server,the security aggregation mechanism can be compromised.b)When using differential privacy in combination with function encryption,the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible.In this paper,we propose a hybrid privacy-preserving scheme for federated learning,called Fed-DFE.Specifically,we present a decentralized multi-client function encryption algorithm.It replaces the TTP in traditional function encryption with an interactive key generation algorithm,avoiding the problem of collusion.Then,an embedded incentive mechanism is designed for function encryption.It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy.Subsequently,we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm.The experimental results demonstrate the effectiveness and efficiency of our scheme.