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Nonreciprocal electrical transport, characterized by an asymmetric relationship between current and voltage, plays a crucial role in modern electronic industries. Recent studies have extended this phenomenon to superconductors, introducing the concept of the superconducting diode effect (SDE). The SDE is characterized by unequal critical supercurrents along opposite directions. Due to the requirement on broken inversion symmetry, the SDE is commonly accompanied by electrical magnetochiral anisotropy (eMCA) in the resistive state. Achieving a magnetic field-free SDE with field tunability is pivotal for advancements in superconductor devices. Conventionally, the field-free SDE has been achieved in Josephson junctions by intentionally intercalating an asymmetric barrier layer. Alternatively, internal magnetism was employed. Both approaches pose challenges in the selection of superconductors and fabrication processes, thereby impeding the development of SDE. Here, we present a field-free SDE in FeTe0.7Se0.3 (FTS) junction with eMCA, a phenomenon absent in FTS single nanosheets. The field-free property is associated with the presence of a gradient oxide layer on the upper surface of each FTS nanosheet, while the eMCA is linked to spin-splitting arising from the absence of inversion symmetry. Both the SDE and eMCA respond to magnetic fields with distinct temperature dependencies. This work presents a versatile and straightforward strategy for advancing superconducting electronics.

Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data. However, with the development of complex application scenarios such as the Internet of Things (IoT) and Smart Earth, the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands. Therefore, joint resource optimization may be the key solution to the scaling problem. This paper simultaneously addresses the multifaceted challenges of computation and communication, with the growing multiple resource demands. We systematically review the joint allocation strategies for different resources (computation, data, communication, and network topology) in FEL, and summarize the advantages in improving system efficiency, reducing latency, enhancing resource utilization and enhancing robustness. In addition, we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements, indirectly. This work not only provides theoretical support for resource management in federated learning (FL) systems, but also provides ideas for potential optimal deployment in multiple real-world scenarios. By thoroughly discussing the current challenges and future research directions, it also provides some important insights into multi-resource optimization in complex application environments., Comment: This paper has been submitted to CMC-Computers Materials & Continua

The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/BVIB-for-Data-Extraction-Based-on Mutual-Information-in-the-IoV

Recent progress in Multimodal Large Language Models(MLLMs) often use large image tokens to compensate the visual shortcoming of MLLMs, which not only exhibits obvious redundancy but also greatly exacerbates the already high computation. Token pruning is an effective solution for speeding up MLLMs, but when and how to drop tokens still remains a challenge. In this paper, we propose a novel and training-free approach for the effective visual token pruning of MLLMs, termed FitPrune, which can quickly produce a complete pruning recipe for MLLMs according to a pre-defined budget. Specifically, FitPrune considers token pruning as a statistical problem of MLLM and its objective is to find out an optimal pruning scheme that can minimize the divergence of the attention distributions before and after pruning. In practice, FitPrune can be quickly accomplished based on the attention statistics from a small batch of inference data, avoiding the expensive trials of MLLMs. According to the pruning recipe, an MLLM can directly remove the redundant visual tokens of different examples during inference. To validate FitPrune, we apply it to a set of recent MLLMs, including LLaVA-1.5, LLaVA-HR and LLaVA-NEXT, and conduct extensive experiments on a set of benchmarks. The experimental results show that our FitPrune can not only reduce the computational complexity to a large extent, while retaining high performance, e.g., -54.9% FLOPs for LLaVA-NEXT with only 0.5% accuracy drop. Notably, the pruning recipe can be obtained in about 5 minutes. Our code is available at https://github.com/ywh187/FitPrune.

Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.

In this paper, we revisit the multistage spectral risk minimization models proposed by Philpott et al.~\cite{PdF13} and Guigues and R\"omisch \cite{GuR12} but with some new focuses. We consider a situation where the decision maker's (DM's) risk preferences may be state-dependent or even inconsistent at some states, and consequently there is not a single deterministic spectral risk measure (SRM) which can be used to represent the DM's preferences at each stage. We adopt the recently introduced average randomized SRM (ARSRM) (in \cite{li2022randomization}) to describe the DM's overall risk preference at each stage. To solve the resulting multistage ARSRM (MARSRM) problem, we apply the well-known stochastic dual dynamic programming (SDDP) method which generates a sequence of lower and upper bounds in an iterative manner. Under some moderate conditions, we prove that the optimal solution can be found in a finite number of iterations. The MARSRM model generalizes the one-stage ARSRM and simplifies the existing multistage state-dependent preference robust model \cite{liu2021multistage}, while also encompassing the mainstream multistage risk-neutral and risk-averse optimization models \cite{GuR12,PdF13}. In the absence of complete information on the probability distribution of the DM's random preferences, we propose to use distributionally robust ARSRM (DR-ARSRM) to describe the DM's preferences at each stage. We detail computational schemes for solving both MARSRM and DR-MARSRM. Finally, we examine the performance of MARSRM and DR-MARSRM by applying them to an asset allocation problem with transaction costs and compare them with standard risk neutral and risk averse multistage linear stochastic programming (MLSP) models., Comment: 33 pages, 4 figures and 3 tables

Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Unit (RSU), ensuring timely services. Our previous work FLSimCo algorithm, which uses local resources for Federated Self-Supervised Learning (SSL), though vehicles often can't complete all iterations task. Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training. Meanwhile, setting an offloading threshold further prevents inefficiencies. Simulation results show that the enhanced algorithm reduces energy consumption, improves offloading efficiency and the accuracy of Federated SSL., Comment: This paper has been submitted to Digital Communications and Networks. The source code has been released at: https://github.com/qiongwu86/Federated-SSL-task-offloading-and-resource-allocation

In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated Self-Supervised Learning (FSSL) utilizes Self-Supervised Learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to Base Station (BS), which poses a risk of privacy leakage. Simplified Contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a Deep Reinforcement Learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the Central Processing Unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/DRL-BFSSL

Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RSU). This enables FL to handle scenarios with sensitive or widely distributed data. However, in these fields, it is well known that the labeling costs can be a significant expense, and models relying on labels are not suitable for these rapidly evolving fields especially in vehicular networks, or mobile internet of things (MIoT), where new data emerges constantly. To handle this issue, the self-supervised learning paves the way for training without labels. Additionally, for vehicles with high velocity, owing to blurred images, simple aggregation not only impacts the accuracy of the aggregated model but also reduces the convergence speed of FL. This paper proposes a FL algorithm based on image blur level to aggregation, called FLSimCo, which does not require labels and serves as a pre-training stage for self-supervised learning in the vehicular environment. Simulation results demonstrate that the proposed algorithm exhibits fast and stable convergence., Comment: This paper has been submitted to urban lifeline. The source code has been released at: The source code has been released at: https://github.com/qiongwu86/FLSimCo

As development Internet-of-Vehicles (IoV) technology and demand for Intelligent Transportation Systems (ITS) increase, there is a growing need for real-time data and communication by vehicle users. Traditional request-based methods face challenges such as latency and bandwidth limitations. Mode 4 in Connected Vehicle-to-Everything (C-V2X) addresses latency and overhead issues through autonomous resource selection. However, Semi-Persistent Scheduling (SPS) based on distributed sensing may lead to increased collision. Non-Orthogonal Multiple Access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Moreover, the concept of Age of Information (AoI) is introduced as a comprehensive metric reflecting reliability and latency performance, analyzing the impact of NOMA on C-V2X communication system. AoI indicates the time a message spends in both local waiting and transmission processes. In C-V2X, waiting process can be extended to queuing process, influenced by packet generation rate and Resource Reservation Interval (RRI). The transmission process is mainly affected by transmission delay and success rate. In C-V2X, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collision rates with increased number of vehicles. SW is generally equal to RRI, which not only affects AoI in queuing process but also AoI in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. This work aims to gain a better performance of C-V2X system comparing with some known algorithms., Comment: This paper has been submitted to WCSP 2024. The source code has been released at: https://github.com/qiongwu86/Analysis-of-the-Impact-of-Multi-Priority-Queue-and-NOMA-on-Age-of-Information-in-C-V2X

Recently, mixture of experts (MoE) has become a popular paradigm for achieving the trade-off between modal capacity and efficiency of multi-modal large language models (MLLMs). Different from previous efforts, we are dedicated to exploring the dynamic expert path in an already exist MLLM and show that a standard MLLM can be also a mixture of experts. To approach this target, we propose a novel dynamic expert scheme for MLLMs, termed Routing Experts (RoE), which can achieve example-dependent optimal path routing without obvious structure tweaks. Meanwhile, a new regularization of structure sparsity is also introduced to enforce MLLMs to learn more short-cut inference, ensuring the efficiency. In addition, we also realize the first attempt of aligning the training and inference schemes of MLLMs in terms of network routing. To validate RoE, we apply it to a set of latest MLLMs, including LLaVA-1.5, LLaVA-HR and VILA, and conduct extensive experiments on a bunch of VL benchmarks. The experiment results not only show the great advantages of our RoE in improving MLLMs' efficiency, but also yield obvious advantages than MoE-LLaVA in both performance and speed, e.g., an average performance gain of 3.3% on 5 benchmarks while being faster.

Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of internet of vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of Reconfigurable Intelligent Surfaces (RIS), which provide alternative communication pathways to assist vehicular communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider a RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, Twin Delayed Deep Deterministic Policy Gradient (TD3) and some typical stochastic schemes., Comment: This paper has been submitted to IEEE Journal. The source code has been released at https://github.com/qiongwu86/DDPG-RIS-MADDPG-POWER. arXiv admin note: text overlap with arXiv:2406.11318

With the increasing demand for multiple applications on internet of vehicles. It requires vehicles to carry out multiple computing tasks in real time. However, due to the insufficient computing capability of vehicles themselves, offloading tasks to vehicular edge computing (VEC) servers and allocating computing resources to tasks becomes a challenge. In this paper, a multi task digital twin (DT) VEC network is established. By using DT to develop offloading strategies and resource allocation strategies for multiple tasks of each vehicle in a single slot, an optimization problem is constructed. To solve it, we propose a multi-agent reinforcement learning method on the task offloading and resource allocation. Numerous experiments demonstrate that our method is effective compared to other benchmark algorithms., Comment: This paper has been submitted to ICICSP 2024. The source code has been released at:https://github.com/qiongwu86/Digital-Twin-Vehicular-Edge-Computing-Network_Task-Offloading-and-Resource-Allocation

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Distributed-Deep-Reinforcement-Learning-Based-Gradient Quantization-for-Federated-Learning-Enabled-Vehicle-Edge-Computing

Autonomous driving may be the most important application scenario of next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancement in cellular V2X (C-V2X) with improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur, and thus degrade the age of information (AOI). Therefore, a interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation interval (RRI), higher-frequency transmissions take ore energy to reduce AoI. Hence, it is important to jointly consider AoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations have demonstrated the performance of our proposed algorithm., Comment: This paper has been accepted by sensors. The source code has been released at: https://github.com/qiongwu86/Joint-Optimization-of-AoI-and-Energy-Consumption-in-NR-V2X-System-based-on-DRL

The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interactions, catalyst design has long relied on trial-and-error, a costly and labor-intensive process leading to scarce data that is heavily biased towards undesired, low-yield catalysts. Despite the rise of ML in this field, most efforts have not focused on dealing with the challenges presented by such experimental data. To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components. This framework combines a series of ML practices designed to handle the scarcity and imbalance of catalyst data. We apply the framework to classify the yield of various catalyst compositions in oxidative methane coupling, and use it to evaluate the performance of a range of ML models: tree-based models, logistic regression, support vector machines, and neural networks. These experiments demonstrate that the methods used in our framework lead to a significant improvement in the performance of all but one of the evaluated models. Additionally, the decision-making process of each ML model is analyzed by identifying the most important features for predicting catalyst performance using XAI methods. Our analysis found that XAI methods, providing class-aware explanations, such as Layer-wise Relevance Propagation, identified key components that contribute specifically to high-yield catalysts. These findings align with chemical intuition and existing literature, reinforcing their validity. We believe that such insights can assist chemists in the development and identification of novel catalysts with superior performance.

As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an emerging technology, can predict, estimate, and analyze real-time states by digitally modeling objects in the physical world. By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles. However, maintaining the vehicle DT model requires ongoing attention from the VEC server, which also needs to offer computing services for the vehicles. Therefore, effective allocation and scheduling of VEC server resources are crucial. This study focuses on a general VEC network with a single VEC service and multiple vehicles, examining the two types of delays caused by twin maintenance and computational processing within the network. By transforming the problem using satisfaction functions, we propose an optimization problem aimed at maximizing each vehicle's resource utility to determine the optimal resource allocation strategy. Given the non-convex nature of the issue, we employ multi-agent Markov decision processes to reformulate the problem. Subsequently, we propose the twin maintenance and computing task processing resource collaborative scheduling (MADRL-CSTC) algorithm, which leverages multi-agent deep reinforcement learning. Through experimental comparisons with alternative algorithms, it demonstrates that our proposed approach is effective in terms of resource allocation., Comment: This paper has been submitted to IEEE Journal. The source code has been released at:https://github.com/qiongwu86/Resource-allocation-for-twin-maintenance-and-computing-tasks-in-digital-twin-mobile-edge-network

In this paper, we propose a novel transmissive reconfigurable intelligent surface transceiver-enhanced robust and secure integrated sensing and communication network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise, while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent-based second-order cone programming algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein's inequality and successive convex approximation are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency and the Cram\'{e}r-Rao boundary.

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments., Comment: 14 pages, 11 figures. This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/GNN-and-DRL-Based-Resource-Allocation-for-V2X-Communications

To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized by dynamically controlling the beam-splitting ratio between the two channels according to the loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.

With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learning