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Affiliation of Author(s):计算机科学与工程学院
Journal:IEEE Internet of Things Journal
Funded by:省、市、自治区科技项目
Key Words:Federated Learning, Privacy-Preservation, Secure Multi-party Computing, FedAVG Algorithm.
Abstract:Federated learning is a promising new technology in the field of IoT intelligence. However, exchanging modelrelated data in federated learning may leak the sensitive information of participants. To address this problem, we propose a novel privacy-preserving FL framework based on an innovative chained secure multi-party computing technique, named ChainPPFL. Our scheme mainly leverages two mechanisms: SingleMasking mechanism which protects information exchanged between participants; Chained-Communication mechanism which enables masked information to be transferred between participants with a serial chain frame. We conduct extensive simulation-based experiments using two public datasets (MNIST and CIFAR-100) by comparing both training accuracy and leakdefence with other state-of-the-art schemes. We set two data sample distributions (IID and Non-IID) and three training models (CNN, MLP and L-BFGS) in our experiments. The experimental results demonstrate that the Chain-PPFL scheme can achieve a practical privacy-preservation (equivalent to differential privacy with approaching zero) for federated learning with some cost of communication and without impairing the accuracy and convergence speed of the training model.
Co-author:Yipeng Zhou,Alireza Jolfaei,俞东进,徐高潮,Xi Zheng
First Author:liyong
Indexed by:Journal paper
Volume:8
Issue:8
Page Number:1
Number of Words:1
ISSN No.:2327-4662
Translation or Not:no
Date of Publication:2021-04-15