Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications

TL;DR

This paper evaluates nine Swarm Intelligence algorithms for client selection in Federated Learning, demonstrating GWO's superior performance in cybersecurity scenarios with diverse and adversarial data conditions.

Contribution

It introduces a comprehensive evaluation of SI algorithms for client selection in FL, highlighting GWO's robustness and effectiveness in cybersecurity applications.

Findings

GWO outperforms other SI algorithms in accuracy and robustness.

PSO and Cuckoo Search also show strong performance.

SI algorithms can effectively address decentralized and adversarial FL challenges.

Abstract

This study addresses a critical gap in the literature regarding the use of Swarm Intelligence Optimization (SI) algorithms for client selection in Federated Learning (FL), with a focus on cybersecurity applications. Existing research primarily explores optimization techniques for centralized machine learning, leaving the unique challenges of client diveristy, non-IID data distributions, and adversarial noise in decentralized FL largely unexamined. To bridge this gap, we evaluate nine SI algorithms-Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Cuckoo Search, Bat Algorithm, Bee Colony, Ant Colony Optimization, Fish Swarm, Glow Worm, and Intelligent Water Droplet-across four experimental scenarios: fixed client participation, dynamic participation patterns, hetergeneous non-IID data distributions, and adversarial noise conditions. Results indicate that GWO exhibits superior adaptability and robustness, achieving the highest accuracy, recall and F1-scoress across all configurations, while PSO and Cuckoo Search also demonstrate strong performance. These findings underscore the potential of SI algorithms to address decentralized and adversarial FL challenges, offereing scalable and resilient solutions for cybersecurity applications, including intrusion detection in IoT and large-scale networks.