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We investigate a cooperative federated learning framework among devices for mobile edge computing, named CFLMEC, where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of…
In this paper, we examine cloud-edge-terminal IoT networks, where edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a central policy for each task can be constructed at a cloud server. The central policy…
Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a…
Existing multi-agent coordination techniques are often fragile and vulnerable to anomalies such as agent attrition and communication disturbances, which are quite common in the real-world deployment of systems like field robotics. To better…
The application of deep reinforcement learning in multi-agent systems introduces extra challenges. In a scenario with numerous agents, one of the most important concerns currently being addressed is how to develop sufficient collaboration…
Mobile edge computing (MEC) is a promising approach for enabling cloud-computing capabilities at the edge of cellular networks. Nonetheless, security is becoming an increasingly important issue in MEC-based applications. In this paper, we…
This article provides an overview of mobile edge computing (MEC) and artificial intelligence (AI) and discusses the mutually-beneficial relationship between them. AI provides revolutionary solutions in nearly every important aspect of the…
Deep Reinforcement Learning (DRL) has emerged as a powerful solution for meeting the growing demands for connectivity, reliability, low latency and operational efficiency in advanced networks. However, most research has focused on…
Researchers all over the world are employing a variety of analysis approaches in attempt to provide a safer and faster solution for sharing resources via a Multi-access Edge Computing system. Multi-access Edge Computing (MEC) is a…
To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency.…
The rising aging population has increased the importance of fall detection (FD) systems as an assistive technology, where deep learning techniques are widely applied to enhance accuracy. FD systems typically use edge devices (EDs) worn by…
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible…
By pushing computation, cache, and network control to the edge, mobile edge computing (MEC) is expected to play a leading role in fifth generation (5G) and future sixth generation (6G). Nevertheless, facing ubiquitous fast-growing…
With the rapid growth of IoT devices and latency-sensitive applications, the demand for both real-time and energy-efficient computing has surged, placing significant pressure on traditional cloud computing architectures. Mobile edge…
Deep reinforcement learning has been applied successfully to solve various real-world problems and the number of its applications in the multi-agent settings has been increasing. Multi-agent learning distinctly poses significant challenges…
To support emerging real-time monitoring and control applications, the timeliness of computation results is of critical importance to mobile-edge computing (MEC) systems. We propose a performance metric called age of task (AoT) based on the…
This paper introduces EdgeAgentX, a novel framework integrating federated learning (FL), multi-agent reinforcement learning (MARL), and adversarial defense mechanisms, tailored for military communication networks. EdgeAgentX significantly…
Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC, mobile computation offloading (MCO), is to…
The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm. Federated learning enables model training at the edge,…
Scheduling the transmission of time-sensitive data to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users under a constraint on the…