Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 7, Issue 11, Pages 10782-10793Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2987958
Keywords
Federated learning; fog computing; Internet of Things (IoT); privacy preserving
Categories
Funding
- National Natural Science Foundation of China [61572255, U1836210, 61802186, 61941116]
- Fundamental Research Funds for the Central Universities [30920021129]
- Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, NJUPT [BDSIP1909]
- CERNET Innovation Project [NGII20190405]
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Federated learning can combine a large number of scattered user groups and train models collaboratively without uploading data sets, so as to avoid the server collecting user sensitive data. However, the model of federated learning will expose the training set information of users, and the uneven amount of data owned by users in multiple users' scenarios will lead to the inefficiency of training. In this article, we propose a privacypreserving federated learning scheme in fog computing. Acting as a participant, each fog node is enabled to collect Internetof-Things (IoT) device data and complete the learning task in our scheme. Such design effectively improves the low training efficiency and model accuracy caused by the uneven distribution of data and the large gap of computing power. We enable IoT device data to satisfy e-differential privacy to resist data attacks and leverage the combination of blinding and Paillier homomorphic encryption against model attacks, which realize the security aggregation of model parameters. In addition, we formally verified our scheme can not only guarantee both data security and model security but completely resist collusion attacks launched by multiple malicious entities. Our experiments based on the Fashion-MNIST data set prove that our scheme is highly efficient in practice.
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