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.
Authors
- Genevieve Okafor
- U. H. Okafor
- Farouk G. Adewumi
Institutions
- PricewaterhouseCoopers (United States) (US)
- Edwards Lifesciences (United States) (US)
- Morgan State University (US)
- Edwards Lifesciences (Switzerland) (CH)
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
- 0.00