Publication: Biasing federated learning with a new adversarial graph attention network
dc.contributor.coauthor | Li K., Zheng J., Ni W., Huang H., Lio P., Dressler F. | |
dc.contributor.department | Department of Electrical and Electronics Engineering | |
dc.contributor.department | Next Generation and Wireless Communication Laboratory | |
dc.contributor.kuauthor | Akan, Özgür Barış | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | Laboratory | |
dc.date.accessioned | 2025-03-06T20:58:34Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an aim to bias the learning process across the FL. The proposed AGAT is developed to synthesize malicious, biasing model updates, where the minimum of Kullback-Leibler (KL) divergence between the user's model update and the global model is maximized. Due to a limited set of labeled input-output biasing data samples, a surrogate model is created, which presents the behavior of a complex malicious model update. Moreover, a graph autoencoder (GAE) is designed within the AGAT architecture, which is trained together with sub- gradient descent to reconstruct manipulatively the correlations of the model updates, and maximize the reconstruction loss while keeping the malicious, biasing model updates undetectable. The proposed AGAT attack is implemented in PyTorch, showing experimentally that AGAT successfully increases the minimum value of KL divergence of benign model updates by 60.9% and bypasses the detection of existing defense models. The source code of the AGAT attack is released on GitHub. © 2002-2012 IEEE. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.sponsorship | This work was supported by the CISTER Research Unit (UIDP/UIDB/04234/2020) and project ADANET (PTDC/EEICOM/3362/2021), financed by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology);and also supported in part by the AXA Research Fund (AXA Chair for Internet of Everything at Koc\u00B8 University). | |
dc.identifier.doi | 10.1109/TMC.2024.3499371 | |
dc.identifier.grantno | Fundação para a Ciência e a Tecnologia, FCT; AXA Research Fund, AXA; UIDP/UIDB/04234/2020, PTDC/EEICOM/3362/2021 | |
dc.identifier.issn | 1536-1233 | |
dc.identifier.quartile | Q1 | |
dc.identifier.scopus | 2-s2.0-85209749527 | |
dc.identifier.uri | https://doi.org/10.1109/TMC.2024.3499371 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/27502 | |
dc.identifier.wos | 1416196500018 | |
dc.keywords | Adversarial graph attention network | |
dc.keywords | Cyberattacks | |
dc.keywords | Fairness | |
dc.keywords | Feature correlations | |
dc.keywords | Federated learning | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.relation.ispartof | IEEE Transactions on Mobile Computing | |
dc.subject | Electrical and electronics engineering | |
dc.title | Biasing federated learning with a new adversarial graph attention network | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Akan, Özgür Barış | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit1 | Laboratory | |
local.publication.orgunit2 | Department of Electrical and Electronics Engineering | |
local.publication.orgunit2 | Next Generation and Wireless Communication Laboratory | |
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