计算机科学
MNIST数据库
差别隐私
方案(数学)
趋同(经济学)
信息隐私
帧(网络)
联合学习
遮罩(插图)
集合(抽象数据类型)
可验证秘密共享
分布式计算
理论计算机科学
人工智能
计算机网络
数据挖掘
深度学习
计算机安全
艺术
视觉艺术
数学分析
经济
程序设计语言
经济增长
数学
作者
Yong Li,Yipeng Zhou,Alireza Jolfaei,Dongjin Yu,Gaochao Xu,Xi Zheng
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2021-04-15
卷期号:8 (8): 6178-6186
被引量:96
标识
DOI:10.1109/jiot.2020.3022911
摘要
Federated learning (FL) is a promising new technology in the field of IoT intelligence. However, exchanging model-related data in FL 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 multiparty computing technique, named chain-PPFL. Our scheme mainly leverages two mechanisms: 1) single-masking mechanism that protects information exchanged between participants and 2) chained-communication mechanism that enables masked information to be transferred between participants with a serial chain frame. We conduct extensive simulation-based experiments using two public data sets (MNIST and CIFAR-100) by comparing both training accuracy and leak defence with other state-of-the-art schemes. We set two data sample distributions (IID and NonIID) and three training models (CNN, MLP, and L-BFGS) in our experiments. The experimental results demonstrate that the chain-PPFL scheme can achieve practical privacy preservation (equivalent to differential privacy with ∈ approaching zero) for FL with some cost of communication and without impairing the accuracy and convergence speed of the training model.
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