Secure Partial Aggregation: Making Federated Learning More Robust for Industry 4.0 Applications

Jiqiang Gao, Baolei Zhang, Xiaojie Guo, Thar Baker, Min Li, Zheli Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Big data, due to its promotion for industrial intelligence, has become the cornerstone of the Industry 4.0 era. Federated learning , proposed by Google, can effectively integrate data from different devices and different domains to train models under the premise of privacy preservation. Unfortunately, this new training paradigm faces security risks both on the client side and server side. This article proposes a new federated learning scheme to defend from client-side malicious uploads (e.g., backdoor attacks). In addition, we use cryptography techniques to prevent server-side privacy attacks (e.g., membership inference). The secure partial aggregation protocol we designed improves the privacy and robustness of federated learning. The experiments show that models can achieve high accuracy of over 90% with a proper upload proportion, while the accuracy of the backdoor attack decreased from 99.5% to 0% with the best result. Meanwhile, we prove that our protocol can disable privacy attacks.
Original languageEnglish
Pages (from-to)6340 - 6348
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number9
DOIs
Publication statusPublished - 25 Jan 2022

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62032012 and in part by the National Key Research and Development Program of China under Grant 2020YFB1005700.

Publisher Copyright:
© 2005-2012 IEEE.

Keywords

  • Federated learning
  • Industry 4.0
  • Privacy preservation
  • Secure aggregation

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