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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)
Verlagsversion DOI PMC
Closed
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Swarm Learning ; Federated Learning ; Fully Homomorphic Encryption ; Gradient Leakage
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0129-0657
e-ISSN 1793-6462
Zeitschrift International Journal of Neural Systems
Quellenangaben Band: 33, Heft: 8, Seiten: , Artikelnummer: 2350033 Supplement: ,
Verlag World Scientific Publishing
Verlagsort 5 Toh Tuck Link, Singapore 596224, Singapore
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540007-001
Förderungen Departmental Strategic Plan (PSD) of the University of Udine Interdepartmental Project on Artificial Intelligence (2020-2025)
DOI 10.1142/S0129065723500338
WOS ID 000996138600001
Scopus ID 85163185989
PubMed ID 37246573
Erfassungsdatum 2023-10-06
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