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Open Access

ARTICLE

A Comprehensive Evaluation of Distributed Learning Frameworks in AI-Driven Network Intrusion Detection

Sooyong Jeong1,#, Cheolhee Park2,#, Dowon Hong3,*, Changho Seo4
1 Basic Science Research Institution, Kongju National University, Gongju, 32588, Republic of Korea
2 Cyber Security Research Division, Electronics and Telecommunications Research Institute, Daejeon, 34129, Republic of Korea
3 Department of Applied Mathematics, Kongju National University, Gongju, 32588, Republic of Korea
4 Department of Convergence Science, Kongju National University, Gongju, 32588, Republic of Korea
* Corresponding Author: Dowon Hong. Email: email
# These authors contributed equally to this work
(This article belongs to the Special Issue: AI-Driven Intrusion Detection and Threat Analysis in Cybersecurity)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072561

Received 29 August 2025; Accepted 20 November 2025; Published online 12 December 2025

Abstract

With the growing complexity and decentralization of network systems, the attack surface has expanded, which has led to greater concerns over network threats. In this context, artificial intelligence (AI)-based network intrusion detection systems (NIDS) have been extensively studied, and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms. However, most existing works focus on individual distributed learning frameworks, and there is a lack of systematic evaluations that compare different algorithms under consistent conditions. In this paper, we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning (FL), Split Learning (SL), hybrid collaborative learning (SFL), and fully distributed learning—in the context of AI-driven NIDS. Using recent benchmark intrusion detection datasets, a unified model backbone, and controlled distributed scenarios, we assess these frameworks across multiple criteria, including detection performance, communication cost, computational efficiency, and convergence behavior. Our findings highlight distinct trade-offs among the distributed learning frameworks, demonstrating that the optimal choice depends strongly on system constraints such as bandwidth availability, node resources, and data distribution. This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.

Keywords

Network intrusion detection; network security,; distributed learning

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