Achieving resilience in distributed optimization against adversarial attacks

This paper provides an in-depth investigation into developing distributed optimization algorithms that are resilient against adversarial cyber-attacks that compromise nodes and disrupt the optimization process. A resilient distributed optimization algorithm with trusted agents (RDO-T) is proposed that utilizes a subset of trusted nodes to constrain and overcome the impacts of attacks on the network. Theoretical analysis on the convergence properties and performance optimality of RDO-T is provided. Keywords: Optimization Algorithm, Resilient Distribution, Cyber Attacks.

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Publication Details

Journal
Computer Science & IT Research Journal
Published
2025-08-25
DOI
https://doi.org/10.51594/csitrj.v6i7.1991
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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article

Achieving resilience in distributed optimization against adversarial attacks

Genevieve Okafor, U. H. Okafor, Farouk G. Adewumi
Computer Science & IT Research Journal
Adversarial Robustness in Machine Learning
article

Achieving resilience in distributed optimization against adversarial attacks

Genevieve Okafor, U. H. Okafor, Farouk G. Adewumi
article en

Abstract

This paper provides an in-depth investigation into developing distributed optimization algorithms that are resilient against adversarial cyber-attacks that compromise nodes and disrupt the optimization process. A resilient distributed optimization algorithm with trusted agents (RDO-T) is proposed that utilizes a subset of trusted nodes to constrain and overcome the impacts of attacks on the network. Theoretical analysis on the convergence properties and performance optimality of RDO-T is provided. Keywords: Optimization Algorithm, Resilient Distribution, Cyber Attacks.

Computer Science & IT Research JournalVol. 6(7)
PricewaterhouseCoopers (United States) (US), Edwards Lifesciences (United States) (US), Morgan State University (US), Edwards Lifesciences (Switzerland) (CH)
Openalex Percentile: Top 89%
Adversarial Robustness in Machine Learning
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