The low earth orbit (LEO) satellite-borne edge cloud (SEC) and machine learning (ML) based semantic communication (SemCom) are both enabling technologies for 6G systems facilitating computation offloading. Nevertheless, integrating SemCom into the SEC networks for user computation offloading introduces semantic coder updating requirements as well as additional semantic extraction costs. Offloading user computation in SEC networks via SemCom also results in new functional challenges considering, e.g., latency, energy, and privacy. In this paper, we present a novel SemCom-assisted SEC (SemCom-SEC) framework for computation offloading of resource-limited users. We then propose an adaptive pruning-split federated learning (PSFed) method for updating the semantic coder in SemCom-SEC. We further show that the proposed method guarantees training convergence speed and accuracy. This method also improves the privacy of the semantic coder while reducing training delay and energy consumption. In the case of trained semantic coders in service, for the users processing computational tasks, the main objective is to minimise the users’ delay and energy consumption, subject to sustaining users’ privacy and fairness amongst them. This problem is then formulated as an incomplete information mixed integer nonlinear programming (MINLP) problem. A new computational task processing scheduling (CTPS) mechanism is also proposed based on the Rubinstein bargaining game. Simulation results demonstrate the proposed PSFed and game theoretical CTPS mechanism outperforms the baseline solutions reducing delay and energy consumption while enhancing users’ privacy.

Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.

Semantic communication based on machine learning (ML) techniques emerged as a new transmission paradigm that can significantly improve spectrum efficiency. It looks promising for improving the task of offloading quality of service (QoS) for autonomous driving networks (ADNs) where autonomous vehicles require a significant amount of communication with the vehicle edge clouds (VECs). However, in practical ADNs, updating the ML-based semantic communication coder model is affected by various unique factors such as mobility and privacy considerations. Therefore, in ADNs, the conventional ML frameworks are not directly applicable to updating semantic communication coders. In this article, we discuss the unique challenges faced by updating the semantic communication coder in ADNs, and review the existing ML frameworks. To address these challenges, we further propose a privacy-preserving personalized federated learning (3PFL) framework for updating the semantic communication coder in ADNs. Simulation results confirm the effectiveness of 3PFL for this process.

Semantic communication holds promise for integration into future wireless networks, offering a potential enhancement in network spectrum efficiency. However, implementing semantic communication in aerial-aided edge networks (AENs) introduces unique challenges. Within AENs, semantic communication strategically substitutes part of the communication load with the computation load, aiming to boost spectrum efficiency. This departure from traditional communication paradigms introduces novel challenges, particularly in terms of energy efficiency. Furthermore, by adding complexity, the use of a semantic coder based on machine learning (ML) in AENs encounters real-time updating challenges, further amplifying energy costs in these complex and energy-limited environments. To address these challenges, we propose an energy-efficient semantic communication system tailored for AENs. Our approach includes a mathematical analysis of semantic communication energy consumption within AENs. To enhance energy efficiency, we introduce an energy-efficient game-theoretic incentive mechanism (EGTIM) designed to optimize semantic transmission within AENs. Moreover, considering the accurate and energy-efficient updating of semantic coders in AENs, we present a game-theoretic efficient distributed learning (GEDL) framework, building upon the foundations of the renewed EGTIM. Simulation results validate the effectiveness of our proposed EGTIM in improving energy efficiency. Additionally, the presented GEDL framework exhibits remarkable performance by increasing model training accuracy and concurrently decreasing training energy consumption.

Collaborative Intrusion Detection System (CIDS) is an essential technology that enables vehicular ad hoc networks (VANET) to protect against malicious intrusions. CIDS, however, is unable to prevent accidents if an anomalous vehicle is detected. Detecting anomalies and notifying vehicles in the VANET rapidly is thus essential, considering technical challenges such as communication efficiency, vehicle velocity and privacy. In this paper, we propose a novel two-layer privacy-aware trust evaluation CIDS framework, termed 2PT-CIDS, tailored to VANET. In 2PT-CIDS, vehicles and roadside units (RSUs) cooperate efficiently to enhance anomalous vehicle detection and notification. Considering its potential privacy leakage, we then present two types of game-theoretic information incentive mechanisms. In the case of traffic congestion, the privacy-aware incentive mechanism is presented based on the Stackelberg game. A Barycentric Lagrange interpolation (BLI) based algorithm is then proposed to speedy achieve the Nash equilibrium (NE). In the case of traffic smooth, the varying high velocities of vehicles are involved and a noncooperative game-based mechanism is proposed. The optimal NE decision selection is reconstructed as a Markov decision process (MDP) and the NE point is obtained via the designed novel reward-shaping double duelling deep Q network (D3QN) learning algorithm. Simulation results highlight the superiority of 2PT-CIDS over existing CIDS and potential application algorithms for VANET, effectively enhancing anomaly detection and notification considering communication cost and vehicle privacy.

In communication networks, where users are highly mobile, migrating edge servers that are performing services closer to users, i.e., service migration, is essential to maintain the high quality of service (QoS). However, existing dynamic service migration techniques face two distinct challenges: (1) The security and energy consumption of service migration systems need to be optimised urgently; (2) The uncertainty of user movement makes it difficult to develop optimal service migration strategies, especially in future three-dimensional (3-D) communication networks. To address these challenges, we propose a novel energy-efficient secure 3-D dynamic service migration framework for communication networks. We then quantify the cost of service migration based on the proposed framework considering security, energy efficiency and delay and present a solution based on a deep reinforcement learning (DRL) approach to make migration decisions optimally in the 3-D communication network. We also propose a universal formula for measuring the reliability value of intelligent autonomous nodes in order to reduce the energy consumption and delay of the proposed security paradigm and to optimise the service migration decision making. Simulation results demonstrate our proposed migration strategy for 3-D communication network services outperforms the baseline solutions in terms of reducing communication network delay and energy consumption while preserving migration security. Moreover, the results confirm the effectiveness of the proposed reliability value calculation approach applied to improve the QoS in the secured edge networks.