As the network continues to become more complex due to the increased number of devices and ubiquitous connectivity, the trend is shifting from a centralized implementation to decentralization. Similarly, strategies to secure networks are increasingly leaning towards decentralization for its potential to enhance security in future networks with the help of Machine Learning (ML) techniques. In this regard, Distributed Machine Learning (DML) techniques, such as Federated Learning (FL) and Split Learning (SL), are at the forefront of this shift, offering collaborative learning capabilities across network nodes while maintaining data privacy. However, ML requires vast amounts of dedicated computing, memory, bandwidth, and as a consequence, energy resources. Moreover, resource consumption ML techniques used for network security have mostly been overlooked, which presents a glaring challenge for future networks in terms of overall resource utilization. This research emphasizes the importance of understanding the resource consumption patterns of two important DML techniques, i.e., FL and SL, to analyze the consumption of critical resources when deployed for network security. Furthermore, this research draws important insights from a practical comparative analysis of FL and SL in terms of resource consumption patterns and discusses their scope for future network security, such as in 6G, and stirs further research in this area.

Machine learning (ML) has become extremely important in communications due to the increasing complexity of networks and due to its promises of improved performance, efficiency, security, cost reduction, and customized user experience. In wireless networks, ML is researched and used in several domains, from access networks to core networks and network services. In network security, the promises of machine learning are vast, from preventive measures to detection to response and remediation. However, machine learning requires a huge amount of resources, mainly due to the fact that ML operates on data, and data volumes are consistently rising. In this review article, we explore the aspect of resource consumption of ML techniques used for network security and provide a comprehensive review of the current state of research. Moreover, we propose a taxonomy that can be used to classify the methods through which the resource consumption can be reduced for different ML-based network security implementations. The focus of the study encompasses several key aspects pertaining to resource consumption, including energy, computing, memory, latency, bandwidth, and human resources. These resources are critical in improving the efficiency and optimizing the reliability and sustainability of network security solutions. Furthermore, based on the extensive literature review, we summarize key points regarding optimizing resource consumption in ML-based network security solutions. Finally, the challenges and future research directions for resource-efficient, ML-based network security solutions are outlined to aid in the advancement of research in this area.