Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing

Yong Li, Yipeng Zhou, Alireza Jolfaei, Dongjin Yu, Gaochao Xu, Xi Zheng

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)


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 ϵ approaching zero) for FL with some cost of communication and without impairing the accuracy and convergence speed of the training model.

Original languageEnglish
Pages (from-to)6178-6186
Number of pages9
JournalIEEE Internet of Things Journal
Issue number8
Publication statusPublished - 15 Apr 2021
Externally publishedYes


  • FedAVG algorithm
  • federated learning (FL)
  • privacy preservation
  • secure multiparty computing (SMC)


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