With the rapid development of information and network communication technologies, especially in vehicle networking, information security issues have become more prominent. Public-key cryptography is widely used, but traditional PKI/CA systems, which require multi-layered CA institutions for certificate provision, have high construction and maintenance costs. In addition, a large number of network terminals face challenges such as resource constraints, high cross-domain processing requirements, and strict latency demands. These issues impact the user experience and hinder the growth of IoT applications. To address this, we propose a lightweight cross-domain authentication scheme (LWCDA) for intelligent vehicle networks. This article utilises identity-based encryption, adopting the same public key parameters across different domains to enable cross-domain authentication for terminal devices. This scheme optimises identity authentication, key management, and privacy protection, enhancing the efficiency and security of cross-domain authentication while ensuring secure communication in complex environments.

With the development of new generation communication technologies, the future maritime information networks pave the way to promote the exploration of ocean resources. Moreover, the underwater data center (UDC) is considered to be a significant data storage and computing unit in future maritime networks for providing ocean services. However, the current deployment of UDC faces the critical issues, i.e., the long-distance underwater transmission is unreliable and the energy consumption and resources of underwater transmission are overloaded. To address the two critical issues of unreliable data transmission and high resource overheads, in this paper, we present a hybrid semantic data transmission architecture in air-ocean integrated networks, which can perceive the sea surface data accurately and transmit it to the UDC for processing. Specifically, in surface layer, uncrewed aerial vehicles (UAVs) perceive ocean environment and send data to the buoy via non-orthogonal multiple-access (NOMA) transmission to improve the channel utilization. In underwater layer, the buoy sends the collected data to UDC via semantic transmission, while the semantic fidelity metric is utilized to improve the transmission efficiency. A resource allocation problem for energy minimization is formulated to jointly optimize the semantic scaling factor, the NOMA decoding order, the communication and computing resource allocations. We exploit a decomposition approach to transform the problem into two sub-problems, where the optimal resource allocations are obtained by proposing efficient algorithms. Finally, we provide simulations to verify the effectiveness and efficiency of our proposed scheme. The results demonstrate that our proposal has the advantages of lower energy consumption compared to several baseline schemes.

Federated learning (FL) has been regarded as a promising paradigm for enabling distributed model training over resource-limited edge devices. Although FL maintains data locality and enhances model generalization, it faces challenges such as model leakage and pressure from frequent model updates. Some existing schemes, such as differential privacy and model encryption, can partially alleviate these issues while sacrificing the accuracy of the modeling training or increasing the computational overheads in training. To address this issue, we design a low-complexity linear collaborative FL (LCFL) framework to enhance the privacy and accuracy of FL. Specifically, we propose the collaborative secrecy transmission (CST) algorithm by integrating a variant of Shamir's secret-sharing with the model segmentation, which can compresses and encrypts the local models for FL. The decoding complexity of the CST algorithm is only$O(N^{3})$under the compression ratio of$N$, which reduces the communication overhead and computational complexity. We conduct a quantitative analysis of the model error induced by the CST algorithm and derive its closed-form upper bound. Within LCFL, we formulate an optimization problem to maximize the global model accuracy in wireless FL by optimizing compression ratios, bandwidth allocation, and transmit-powers. Subsequently, we propose a low-complexity algorithm to solve this problem effectively. Numerical simulations demonstrate the efficacy of LCFL in improving FL's accuracy and security, and the results validate the efficiency of the proposed optimization scheme for wireless FL. The source code can be downloaded from the Github:https://github.com/MinITerence/LCFL.

As a promising framework for distributed machine learning (ML), wireless federated learning (FL) faces the threat of eavesdropping attacks when a trained ML model is sent over a radio channel. To address this threat, we propose channel-sharing-based artificial jamming to increase the secrecy throughput of FL clients (FCs). Specifically, when an FC performs local model training, a selected device such as a sensor node (SN) not involved in the FL opportunistically accesses the FC’s channel to transmit its sensing data. In return, when the FC sends its locally trained model to the FL server (FLS), the selected SN provides artificial jamming to increase the FC’s secrecy throughput. Considering multiple FCs and SNs, we first consider a given pairing of FCs and SNs and optimize the local training time, the model uploading time, and the transmit-power of the FCs to minimize the total latency of FL training. After proving the convexity of this optimization problem, we propose an efficient algorithm to derive the semi-analytical solution. Then, we further investigate the pairing of the FCs and the SNs to minimize a system-wise cost reflecting both energy consumption and latency. The resulting problem is a bicriteria pairing problem, and we propose an efficient algorithm to compute the optimal pairing solution. Numerical results demonstrate the efficiency and performance advantage of our proposed channel-sharing-based approach with artificial jamming in comparison with different benchmark schemes.

Digital twin (DT), which integrates physical networks and digital space by using advanced technologies of sensing, communication and computation, has been envisioned as a promising paradigm for improving the quality of service in physical systems. In this paper, we propose a federated learning (FL)-enabled DT system consisting of the physical layer and DT layer. With FL, all wireless devices (WDs) can collaborate to update a universal DT model, after the DT server cluster (DSC) aggregates all the local models sent by the WDs with non-orthogonal multiple access (NOMA). Moreover, an action model based on the DT system is also updated to optimize the operations of WDs. To increase the energy efficiency, we formulate a problem to minimize the cost of the total energy consumption of the system by optimizing the time allocation of local training, uploading the local models, generating the action model as well as broadcasting the action model and DT model. The numerical results validate the effectiveness and efficiency of our proposed algorithm.

Federated learning (FL) has been considered as a promising paradigm for enabling distributed training/learning in many machine-learning services without revealing users’ local data. Driven by the growing interests in exploiting FL in wireless networks, this paper studies the Non-orthogonal Multiple Access (NOMA) assisted FL in which a group of end-devices (EDs) form a NOMA cluster to send their locally trained models to the cellular base station (BS) for model aggregation. In particular, we consider that the BS adopts wireless power transfer (WPT) to power the EDs (for their data transmission and local training) in each round of FL iteration, and formulate a joint optimization of the BS’s WPT for different EDs, the EDs’ NOMA-transmission for sending the local models to the BS, the BS’s broadcasting of the aggregated model to all EDs, the processing-rates of the BS and EDs, as well as the training-accuracy of the FL, with the objective of minimizing the system-wise cost accounting for the total energy consumption as well as the FL convergence latency. In spite of the strict non-convexity of the joint optimization problem, we analytically characterize the BS’s and all EDs’ optimal processing-rates, based on which we propose a layered algorithm for finding the optimal solutions for the joint optimization problem via exploiting monotonic optimization. Numerical results validate that our algorithm can achieve the optimal solution as LINGO’s global-solver (i.e., a commercial optimization package) while significantly reducing the computation-time. Moreover, the results also demonstrate that our NOMA assisted FL can reduce the system cost compared to the benchmark FL scheme with the fixed local training-accuracy by more than 70% and the conventional frequency division multiple access (FDMA) based FL by 78%.

Equipped with machine learning (ML) models, unmanned aerial vehicle (UAV) swarms can execute various applications like surveillance and target detection. However, the connections between UAVs and cloud servers cannot be guaranteed, especially when executing massive data. Thus, traditional cloud-centric approach will not be suitable, since it may cause high latency and significant bandwidth consumption. In this work, we propose a federated learning (FL) framework via non-orthogonal multiple access (NOMA) for a UAV swarm which is composed of a leader-UAV and a group of follower-UAVs. Specifically, each follower-UAV updates its local model by using its collected data, and then all follower-UAVs form a NOMA-group to send their respectively trained FL parameters (i.e., the local FL models) to the leader-UAV simultaneously. We formulate a joint optimization of the uplink NOMA-transmission durations, downlink broadcasting duration, as well as the computation-rates of the leader-UAV and all follower-UAVs, aiming at minimizing the latency in executing the FL iterations until reaching a specified accuracy. Numerical results are presented to verify the effectiveness of our proposed algorithm, and demonstrate that the proposed algorithm can outperform some baseline strategies.