Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.

To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.

With the growth of artificial intelligence (AI) and Industry 4.0, construction management has entered a phase of rapid digital transformation. In order to effectively adopt digital applications of construction management, this paper aims to identify the specific applications of AI in construction management from the perspective of Construction 4.0, especially when applying technologies from Industry 4.0. A scoping review methodology was used to explore the limited literature in this research area. 60 articles were selected to analyze the state of the art of AI applications in construction management, especially for schedule management, cost management, quality management, and health and safety management. This review shows that AI has mainly been used in the preliminary design and construction phases of the above management areas, and proposes a research framework to highlight the contemporary development and needs for AI integration in construction management. The main contributions of this paper are its practical exploration of AI applications in construction management, its human-centered approach to AI adoption, and the introduction of a novel research framework to guide industry practitioners in AI integration.

The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited and issues such as privacy concerns and concept drift arise. Condensation techniques offer a solution by reducing the data size, simplifying complex models, and transferring knowledge from traffic data. This paper explores data and knowledge condensation methods—such as coreset selection, data compression, knowledge distillation, and dataset distillation—within the context of traffic classification tasks. It clarifies the relationship between these techniques and network traffic classification, introducing each method and its typical applications. This paper also outlines potential scenarios for applying each condensation technique, highlighting the associated challenges and open research issues. To the best of our knowledge, this is the first comprehensive summary of condensation techniques specifically tailored for network traffic classification tasks.

Software-Defined Networking (SDN) systems are sensitive to the lifespans of flow entries in flow tables as such lifespans affect the overall network-forwarding latency and the flow table space usage, particularly when the systems are under cyber attack. Instead of developing extra approaches to reactively detect the attacks and mitigate the impact of the attacks, this paper views the lifespans of flow entries as the privacy of the SDN systems and proposes a Flow-Entry Aging RandOmization Layer (FEAROL), which applies the staircase randomized response mechanism in the flow-entry-aging process at switches. FEAROL locally perturbs the lifespan of each flow entry in the flow table. Since the true lifespan of each flow entry is different from the lifespan set in the entry by the controller and dynamically perturbed by FEAROL, Low-rate Denial-of-Service (LDoS) attacks based on the sniffed flow-entry timeouts cannot be effectively organized. FEAROL proactively prevents LDoS attacks from overflowing the flow tables and legitimate flow packets from being dropped due to the broken synchronization between attack flows and the network settings. FEAROL can adjust its aging policies and privacy budget based on the real-time monitored network performance. FEAROL is prototyped in an open-source soft switch (OpenVSwitch) and evaluated through simulations on real network traces. The results show that FEAROL increases the overhead of aging a flow entry. However, this overhead can be significantly reduced by adjusting the interval at which the aging process is triggered. FEAROL also effectively defends against flow table overflow LDoS attacks by significantly reducing the table usage of LDoS attack flows. When the aging strategy is carefully chosen, the table space used by attack flow entries can be reduced to 0.

Traditional methods of analyzing and processing computer networks have difficulty dealing with their rapidly increasing scalability, structure, and traffic complexity due to the rapid development of information technology. Graph neural networks (GNNs), an emerging deep learning technology, adapt to graph-structured data and relationships. They offer new mechanisms and solutions to real-world problems. This paper provides a structured, unified synthesis of current research in this interdisciplinary field. It reviews the basic concepts and models of GNNs, highlights their current state in major computer network management and optimization scenarios, and discusses typical applications, challenges, open issues, and future research directions.