Abstract
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 language | English |
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Pages (from-to) | 6178-6186 |
Number of pages | 9 |
Journal | IEEE Internet of Things Journal |
Volume | 8 |
Issue number | 8 |
Early online date | 8 Sept 2020 |
DOIs | |
Publication status | Published - 15 Apr 2021 |
Keywords
- FedAVG algorithm
- federated learning (FL)
- privacy preservation
- secure multiparty computing (SMC)