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.