In 6G networks, Integrated Sensing and Communication (ISAC) is a key technology owing to its capability in enhancing spectral efficiency. Yet, in urban wireless environments, characterized by persistent random interference, maintaining stringent environmental prerequisites is infeasible. This bottleneck renders robust sensing under non-ideal conditions difficultly. In this paper, we propose a paradigm for multi-Base stations (BSs) cooperative sensing. In this paradigm, we simultaneously consider both reflected and scattered signals during the target UAV movement to improve sensing accuracy. As for the multipath signals arising from such paradigm, we have developed a mathematical model for the echo signals received at the ISAC BSs and illustrated how channel time-variability affects the receiving signals. Based on analysis for the time-varying nature of the real channel environment, we introduce a range power gain algorithm based on fuzzy positioning and name it Fuzzy Range Enhancement-Transmission Spatial Gain (FRE-TSG) inspired by Linearly Constrained Minimum Variance (LCMV). Furthermore, to robustly use multiple sensing results, we design a result-level fusion method Data-Driven-Maximum Consensus ISAC (2D-MaCeS) matching algorithm. This algorithm fully exploits the spatiotemporal diversity of multiple sensing results and reduces dependence on prior information. Finally, we validate the efficacy and robustness of the proposed algorithm through extensive simulation experiments.

In the industrial Internet of Things (IIoT), edge computing is a pivotal power in enhancing system efficiency and responsiveness. However, traditional edge computing faces some challenges like poor flexibility in communication and susceptibility to blockages. Autonomous aerial vehicles (AAVs)-assisted edge computing can address these challenges due to their flexible deployment and strong Line of Sight (LoS) link capabilities. But it also confronts challenges like signal attenuation and resource constraints. To solve these problems, reconfigurable intelligent surface (RIS) emerges as a promising integration strategy to enhance network communication and computing capabilities. Integrating AAVs and RISs in complex dynamic edge computing system poses a notable challenge in achieving real-time and efficient decision-making. Digital twin (DT) technology is an advanced technology that establishes real-time mapping and interaction between the physical world and virtual models, thereby providing real-time status monitoring and precise offloading decisions for the system. Therefore, this article considers a novel DT-driven edge computing system supported by AAVs equipped with RIS in IIoT. In this system, we focus on the intelligent computation offloading problem, whose objective is to minimize the maximum execution time across all user devices (UDs). To tackle this nonconvex mixed-integer nonlinear optimization problem, we decompose it into the scheduling and offloading optimization problem and the allocation optimization problem. Then, we first propose a multitask reinforcement learning algorithm to solve the scheduling and offloading optimization problem by optimizing the AAV trajectories, UD offloading choices, and RIS phase shifts. Afterward, based on the solution of the scheduling and offloading optimization problem, we propose an alternating iterative algorithm to address the allocation optimization problem through optimizing the offloading ratio and resource allocation. Finally, through extensive simulation experiments, we validate the effectiveness and feasibility of our proposed solution.

Integrating Connected and Autonomous Vehicles (CAVs) with federated learning (FL) has garnered widespread attention in recent years, particularly in object detection. However, within the Internet of Vehicle (IoV) context, employing FL to handle complex visual tasks faces several challenges, such as difficulties in obtaining labeled data, heterogeneity in user data across vehicles, and limitations in timely assessing user contributions. To address these challenges, we propose a federated semi-supervised learning architecture for object detection, accompanied by a contribution evaluation and aggregation method based on heterogeneous data. Specifically, we designed a federated semi-supervised training process for the IoV, utilizing an object detection framework based on Faster R-CNN and a teacher–student architecture. To demonstrate its effectiveness, we conducted a communication feasibility analysis using real-world vehicular network data and an analysis of the algorithm’s convergence properties. Additionally, we developed a data-based user contribution assessment and aggregation framework to evaluate the distribution and quality of data from vehicle users to aid the FL center. Finally, simulation results show that the proposed federated semi-supervised algorithm can effectively train and converge to a model that outperforms traditional FL. Ablation experiments further validate the efficacy of the data-based assessment method.

Federated learning (FL) is a promising distributed learning framework in terms of privacy protection and communication saving. Most existing FL techniques are developed for independent-and-identically-distributed (IID) datasets, but suffer from performance degradation under Non-IID datasets. To cope with this issue, most existing work designs solutions from data perspectives (e.g., sharing some data samples between local devices) to eliminate the heterogeneity of distributed datasets, which causes extra communication overhead and may expose user privacy that contradicts FL's original intention. Unlike the existing data-based methods, we propose a generative adversarial network (GAN) based FL, named as GANFed, which is designed from a feature perspective. Specifically, we embed a discriminator into the FL network, which works with the shallow layers as a generator to form a GAN in FL. By incorporating such a GAN, the output of the shallow layers tends to present more IID features compared with the original Non-IID input data. These extracted features from the shallow layers are then used to train the deep layers of the FL network. In this way, the proposed GANFed reduces the weight divergence of the local models, and hence improves the performance of FL. Without data exchange, our GANFed avoids the leakage of user privacy and reduces the communication overhead. Experimental results show that our GANFed outperforms the standard FedAvg on Non-IID dataset in terms of improved test accuracy.

Vehicular Edge Computing and Networks (VECoNs) have gained popularity for its enhanced Internet of Vehicles (IoV) capabilities. To satisfy the needs of delay-sensitive and computation-intensive in-vehicle applications, VECoNs need to provide low-latency task offloading services. However, existing offloading frameworks generally overlook the spatially and temporally heterogeneous computation task arrival patterns. The former causes overloading and underloading of RSU computational resources and thus hinders further reduction of offloading latency on the macro-scale, while the latter emphasizes the importance of long-term system performance, especially energy constraints, posing challenges to the design of offloading framework and optimization strategies. This paper introduces a novel distributed two-stage task offloading architecture based on Lyapunov and multi-agent deep deterministic policy gradient (MADDPG). On one hand, it jointly optimizes the initial offloading stage within VEC subsystems and the RSU peer offloading stage to minimize offloading delays for each VEC subsystem. On the other hand, it incorporates RSU energy consumption within long-term constraints to formulate the offloading optimization problem. After decoupling the energy coupling between RSU time slots using the Lyapunov algorithm, a Lyapunov and MADDPG-based distributed task offloading (LAMETO) algorithm is presented to solve the optimal problem in a distributed manner. Simulation results show that the proposed framework and algorithm can reduce the system delay, energy consumption, and energy deficit while stabilizing convergence.

In this paper, we study a communication-efficient distributed learning scheme through a holistic integration of federated learning (FL) and particle swarm optimization, called DSL, which is suitable for the implementation of intelligent IoT applications. Since only one selected optimum from all local devices need to report its local model updates to the parameter server, the communication cost of DSL is much reduced compared to its counterpart of standard FL. However, the DSL is vulnerable to adversarial attackers. To achieve Byzantine-resilient DSL, we propose to introduce a shared dataset for scoring local updates to screen attackers. We further provide the convergence analysis to theoretically demonstrate that CB-DSL is superior than the standard FL. Experiment results show that the learning performance of our proposed CB-DSL outperforms the existing benchmarks with only a small amount of globally shared data. It enjoys higher robustness against Byzantine attacks than the vanilla DSL, and has better communication efficiency than the standard FL11Our code can be found at: https://github.com/fuanxiyin/CB-DSL.git..

Federated learning (FL) methods face major challenges including communication bottleneck, data heterogeneity and security concerns in edge IoT scenarios. In this paper, inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with non-independent and identically distributed (non-i.i.d.) data issues and Byzantine attacks, a very small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Further, we provide convergence analysis to theoretically demonstrate that the proposed CB-DSL is superior to the standard FL with better convergence behavior. In addition, to measure the effectiveness of the introduction of the globally shared dataset, we also evaluate the model divergence by deriving its upper bound. Numerical results verify that the proposed CB-DSL outperforms the existing benchmarks in terms of faster convergence speed, higher convergent accuracy, lower communication cost, and better robustness against non-i.i.d. data and Byzantine attacks11Our code can be found at:https://github.com/fuanxiyin/CB-DSL.git..

For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog-aggregation transmissions. To facilitate design parameter optimization, we analyze the efficacy of the proposed scheme by deriving a closed-form expression for the expected convergence rate. Our theoretical results unveil the tradeoff between convergence performance and communication efficiency as a result of the aggregation errors caused by sparsification, dimension reduction, quantization, signal reconstruction and noise. Then, we formulate a joint optimization problem to mitigate the impact of these aggregation errors through joint optimal design of worker scheduling and power scaling policy. An enumeration-based method is proposed to solve this non-convex problem, which is optimal but becomes computationally infeasible as the number of devices increases. For scalable computing, we resort to the alternating direction method of multipliers (ADMM) technique to develop an efficient implementation that is suitable for large-scale networks. Simulation results show that our proposed 1-bit CS based FL over the air achieves comparable performance to the ideal case where conventional FL without compression and quantification is applied over error-free aggregation, at much reduced communication overhead and transmission latency.

In this paper, we focus on federated learning (FL) over the air based on analog aggregation transmission in realistic wireless networks. We first derive a closed-form expression for the expected convergence rate of FL over the air, which theoretically quantifies the impact of analog aggregation on FL. Based on that, we further develop a joint optimization model for accurate FL implementation, which allows a parameter server to select a subset of edge devices and determine an appropriate power scaling factor. Such a joint optimization of device selection and power control for FL over the air is then formulated as an mixed integer programming problem. Finally, we efficiently solve this problem via a simple finite-set search method. Simulation results show that the proposed solutions developed for wireless channels outperform a benchmark method, and could achieve comparable performance of the ideal case where FL is implemented over reliable and error-free wireless channels.

As a promising distributed learning technology, analog aggregation-based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm. When all edge devices (workers) simultaneously upload their local updates to the parameter server (PS) through commonly shared time-frequency resources, the PS obtains the averaged update only rather than the individual local ones. While such a concurrent transmission and aggregation scheme reduces the latency and communication costs, it unfortunately renders FLOA vulnerable to Byzantine attacks. Aiming at Byzantine-resilient FLOA, this article starts from analyzing the channel inversion (CI) mechanism that is widely used for power control in FLOA. Our theoretical analysis indicates that although CI can achieve good learning performance in the benign scenarios, it fails to work well with limited defensive capability against Byzantine attacks. Then, we propose a novel scheme called the best effort voting (BEV) power control policy that is integrated with stochastic gradient descent (SGD). Our BEV-SGD enhances the robustness of FLOA to Byzantine attacks, by allowing all the workers to send their local updates at their maximum transmit power. Under worst-case attacks, we derive the expected convergence rates of FLOA with CI and BEV power control policies, respectively. The rate comparison reveals that our BEV-SGD outperforms its counterpart with CI in terms of better convergence behavior, which is verified by experimental simulations.

Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy. Nonetheless, non-ideal communication links and limited transmission resources may hinder the implementation of fast and accurate FL. In this paper, we study joint optimization of communications and FL based on analog aggregation transmission in realistic wireless networks. We first derive closed-form expressions for the expected convergence rate of FL over the air, which theoretically quantify the impact of analog aggregation on FL. Based on the analytical results, we develop a joint optimization model for accurate FL implementation, which allows a parameter server to select a subset of workers and determine an appropriate power scaling factor. Since the practical setting of FL over the air encounters unobservable parameters, we reformulate the joint optimization of worker selection and power allocation using controlled approximation. Finally, we efficiently solve the resulting mixed-integer programming problem via a simple yet optimal finite-set search method by reducing the search space. Simulation results show that the proposed solutions developed for realistic wireless analog channels outperform a benchmark method, and achieve comparable performance of the ideal case where FL is implemented over error-free wireless channels.

With the great-leap-forward development of Internet of Things (IoT), it is extremely important to study secure wireless transmission for IoT systems. A cooperative jamming (CJ) strategy has been extensively studied to enhance physical layer security in IoT systems. However, CJ's secrecy performance has not been well studied in the scenario of collusive eavesdroppers. In this paper, we propose a CJ scheme for IoT systems to fight against multiple passive and collusive eavesdroppers of unknown channel state information. Considering nodes with multi-antenna in an IoT system, we design beamforming vectors to maximize signal-to- interference-plus-noise ratio (SINR) of the wireless link from a controller (transmitter) to an actuator (receiver). Under the worst-case assumption that eavesdroppers can aggregate all signals to enhance their eavesdropping abilities, we derive the closed- form expressions of the secrecy outage probability (SOP) with and without CJ. In addition, we employ a strict mathematical asymptotic analysis to provide insights into the effects of various system parameters on the SOP. Numerical results verify that the CJ scheme can effectively prevent eavesdropping and the effects of system parameters on SOP is consistent with theoretical analytical results.

Cooperative jamming is deemed as a promising physical layer-based approach to secure wireless transmissions in the presence of eavesdroppers. In this paper, we investigate cooperative jamming in a two-tier 5G heterogeneous network (HetNet), where the macrobase stations (MBSs) at the macrocell tier are equipped with large-scale antenna arrays to provide space diversity and the local base stations (LBSs) at the local cell tier adopt non-orthogonal multiple access (NOMA) to accommodate dense local users (LUs). In the presence of imperfect channel state information, we propose three robust secrecy transmission algorithms that can be applied to various scenarios with different security requirements. The first algorithm employs robust beamforming (RBA) that aims to optimize the secrecy rate of a marcouser (MU) in a macrocell. The second algorithm provides robust power allocation (RPA) that can optimize the secrecy rate of an LU in a local cell. The third algorithm tackles a robust joint optimization (RJO) problem across tiers that seek the maximum secrecy sum rate of a target MU and a target LU robustly. We employ convex optimization techniques to find feasible solutions to these highly non-convex problems. The numerical results demonstrate that the proposed algorithms are highly effective in improving the secrecy performance of a two-tier HetNet.

An underlay cognitive radio network (CRN) with non- orthogonal multiple access (NOMA) is a promising multiple access scheme to solve the problem of scarce spectrum. This novel NOMA-enabled underlay CRN can also enhance the transmission secrecy via employing the deliberately introduced interference. In this paper, we intend to investigate the secrecy capacity of a pair of primary users (PUs) and randomly deployed secondary users (SUs) in the NOMA- enabled underlay CRN. Considering an existing eavesdropper in the network, we derive a closed-form expression of the secrecy sum rate (SSR) of all SUs. Then, we further formulate an SSR optimization problem for both PUs and SUs and design a simulated annealing algorithm to find the optimal power allocation. Simulation results demonstrate that the secrecy performance of all NOMA-enabled SUs is higher than that of SUs with frequency division multiple access (FDMA). In other words, the use of the NOMA technology can improve the secrecy performance of the cognitive radio system.

In this paper, we investigate secure communications in energy harvesting enabled cooperative cognitive radio networks (CCRNs). In such CCRNs, a pair of primary users (PUs) can only communicate with each other through a relay. To protect data transmission between PUs, we propose a cooperative jamming strategy with energy harvesting technology. In the first phase, a PU as the source (PU-S) broadcasts signals, while another PU as the destination (PU-D) sends artificial noise (AN) and all secondary uses (SUs) harvest energy from the received mixed signals. In the second phase, one SU selected as a relay (SU-R) uses its harvested energy to forward PU's signals, while another SU selected as a jammer (SU-J) employs its harvested energy to send AN. According to this, we formulate a non-convex problem aiming to improve the secrecy rate of PUs and divide this problem into three optimization subproblems. Finally we provide a feasible joint solution by a two-tiered iterative algorithm. Numerical results demonstrate the secrecy performance of our proposed jamming strategy.