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Madni, H.A.* ; Umer, R.M. ; Foresti, G.L.*

Swarm-fhe: Fully homomorphic encryption based swarm learning for malicious clients.

Int. J. Neural Syst. 33:2350033 (2023)
DOI PMC
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Swarm Learning ; Federated Learning ; Fully Homomorphic Encryption ; Gradient Leakage
ISSN (print) / ISBN 0129-0657
e-ISSN 1793-6462
Journal International Journal of Neural Systems
Quellenangaben Volume: 33, Issue: 8, Pages: , Article Number: 2350033 Supplement: ,
Publisher World Scientific Publishing
Publishing Place 5 Toh Tuck Link, Singapore 596224, Singapore
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Grants Departmental Strategic Plan (PSD) of the University of Udine Interdepartmental Project on Artificial Intelligence (2020-2025)