Analysis of deep learning under adversarial attacks in hierarchical federated learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260596" target="_blank" >RIV/61989100:27240/25:10260596 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S266729522500025X?pes=vor&utm_source=clarivate&getft_integrator=clarivate" target="_blank" >https://www.sciencedirect.com/science/article/pii/S266729522500025X?pes=vor&utm_source=clarivate&getft_integrator=clarivate</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.hcc.2025.100321" target="_blank" >10.1016/j.hcc.2025.100321</a>

Result language

angličtina

Original language name

Analysis of deep learning under adversarial attacks in hierarchical federated learning

Original language description

Hierarchical Federated Learning (HFL) extends traditional Federated Learning (FL) by introducing multilevel aggregation in which model updates pass through clients, edge servers, and a global server. While this hierarchical structure enhances scalability, it also increases vulnerability to adversarial attacks - such as data poisoning and model poisoning - that disrupt learning by introducing discrepancies at the edge server level. These discrepancies propagate through aggregation, affecting model consistency and overall integrity. Existing studies on adversarial behaviour in FL primarily rely on single-metric approaches - such as cosine similarity or Euclidean distance - to assess model discrepancies and filter out anomalous updates. However, these methods fail to capture the diverse ways adversarial attacks influence model updates, particularly in highly heterogeneous data environments and hierarchical structures. Attackers can exploit the limitations of single-metric defences by crafting updates that seem benign under one metric while remaining anomalous under another. Moreover, prior studies have not systematically analysed how model discrepancies evolve over time, vary across regions, or affect clustering structures in HFL architectures. To address these limitations, we propose the Model Discrepancy Score (MDS), a multi-metric framework that integrates Dissimilarity, Distance, Uncorrelation, and Divergence to provide a comprehensive analysis of how adversarial activity affects model discrepancies. Through temporal, spatial, and clustering analyses, we examine how attacks affect model discrepancies at the edge server level in 3LHFL and 4LHFL architectures and evaluate MDS&apos;s ability to distinguish between benign and malicious servers. Our results show that while 4LHFL effectively mitigates discrepancies in regional attack scenarios, it struggles with distributed attacks due to additional aggregation layers that obscure distinguishable discrepancy patterns over time, across regions, and within clustering structures. Factors influencing detection include data heterogeneity, attack sophistication, and hierarchical aggregation depth. These findings highlight the limitations of single-metric approaches and emphasize the need for multi-metric strategies such as MDS to enhance HFL security. (c) 2025 The Author(s). Published by Elsevier B.V. on behalf of Shandong University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

High-Confidence Computing

ISSN

2667-2952

e-ISSN

2667-2952

Volume of the periodical

5

Issue of the periodical within the volume

4

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

23

Pages from-to

nestránkováno

UT code for WoS article

001582659700001

EID of the result in the Scopus database

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