Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients

【Author】 Madni, Hussain Ahmad; Umer, Rao Muhammad; Foresti, Gian Luca

【Source】INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

【影响因子】6.325

【Abstract】Swarm Learning (SL) is a promising approach to perform the distributed and collaborative model training without any central server. However, data sensitivity is the main concern for privacy when collaborative training requires data sharing. A neural network, especially Generative Adversarial Network (GAN), is able to reproduce the original data from model parameters, i.e. gradient leakage problem. To solve this problem, SL provides a framework for secure aggregation using blockchain methods. In this paper, we consider the scenario of compromised and malicious participants in the SL environment, where a participant can manipulate the privacy of other participant in collaborative training. We propose a method, Swarm-FHE, Swarm Learning with Fully Homomorphic Encryption (FHE), to encrypt the model parameters before sharing with the participants which are registered and authenticated by blockchain technology. Each participant shares the encrypted parameters (i.e. ciphertexts) with other participants in SL training. We evaluate our method with training of the convolutional neural networks on the CIFAR-10 and MNIST datasets. On the basis of a considerable number of experiments and results with different hyperparameter settings, our method performs better as compared to other existing methods.

【Keywords】Swarm learning; fully homomorphic encryption; federated learning; gradient leakage

【发表时间】2023 2023 MAY 27

【收录时间】2023-06-20

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习