Federated learning (FL) has been extensively utilized in distributed learning scenarios for the Internet of Vehicles (IoV). However, two key challenges exist: 1) gradients are frequently transmitted between vehicles during FL training, which reduces the communication timeliness between the traffic participants and 2) the fixed gradient compression method cannot sufficiently adapt to the dynamic IoV network topology. Therefore, we design an adaptive vehicle clustering method constructed according to multiple attributes, such as computational resources, communication distance, and latency. Accordingly, we propose a dynamic gradient compression strategy that filters similar local training models between vehicles and uses the Wasserstein distance to compute a sparsity threshold. This threshold acts as a dynamic compression factor that compresses gradient model parameters, reducing redundant parameter transmission. Furthermore, we conduct experiments using two datasets to evaluate the proposed strategy’s effectiveness. The compression ratio improved by 132- and 178-fold compared to the baselines, and the aggregated accuracy increased by an average of 10.13%. Additionally, experiments incorporating communication noise revealed that the aggregation model of the signal noise ratio is -29 dB.

With the rapid advancement of in-vehicle communication technology, vehicular edge caching has garnered considerable attention as a pivotal technology to improve the efficiency of data transmission. However, existing studies often overlook the issues of increased average content access latency and decreased caching hit rate, stemming from the conflict between limited storage space in in-vehicle edge servers and vehicle mobility. To address these issues, this paper proposes a Multi-agent Federated Deep Reinforcement Learning based Collaborative Caching Strategy (MFDRL-CCS), leveraging Vehicle-to-Vehicle (V2V) communications. Specifically, we first perform vehicle connectivity prediction based on Recurrent Neural Network (RNN) considering the characteristics of vehicle nodes and their interrelations. Then, the optimal caching vehicle is selected based on the connectivity between vehicle nodes and the density of vehicle nodes. Meanwhile, a Multi-Head Attention Popularity Prediction (MHAPP) model is also constructed, which amalgamates multi-dimensional features, including historical popularity, social relationships, and geographic location, to predict content popularity. Finally, the edge collaborative caching model is formulated as a Markov Decision Process (MDP). Under the multi-agent competitive deep Q-learning framework, each vehicle learns the optimal caching strategy through an independent Q-network to maximize long-term rewards, and uses federated learning to train the caching replacement algorithm in a distributed manner. Compared to existing caching policies, the caching policy proposed in this paper improves the caching hit rate by approximately 19.8% and reduces the content access latency by about 12.5%.

User alignment refers to linking a user's accounts across multiple social networks, which is important for studying community discovery, recommendation systems, and other related fields. However, existing methods primarily perform user alignment by correlating user features, neglecting the causal relationship between network topology and user alignment, which makes it challenging to achieve superior user alignment accuracy and generalization capabilities. Therefore, we propose a counterfactual inference-based social network user-alignment algorithm (CINUA). This improves user connection retention due to the non-Euclidean geometric characterization of hyperbolic spaces. The similarity of aligned users is augmented using a hyperbolic graph attention network. User-feature embedding and fusion facilitate user relevance mining. Furthermore, there are causal relationships between network topology structure and user linkages. In various communities, there are some highly similar user pairs, and based on counterfactual inference, the network topology is adjusted to enhance sample diversity. Multilevel factual and counterfactual networks are constructed through iterative diffusion based on user alignment and their linkages. By integrating the users’ causal features in multiple networks, the accuracy and generalization capabilities of the user alignment model are effectively improved. In this article, the experimental results indicate that CINUA achieves a user alignment accuracy improvement of 5.98% and 3.03%, on two datasets respectively compared to the baseline methods on average. CINUA can achieve favorable alignment results even when the training dataset is small. This demonstrates that our algorithm can ensure both user alignment accuracy and generalization capability.

For physical-layer secrecy performance, existing studies only focus on coding schemes that can achieve the secrecy capacity. However, finite blocklength penalty could be fatal for security of private message. In this paper, we explore the impact of finite blocklength on secrecy for the wiretap channel model. We propose secrecy performance matrices for finite blocklength analysis and provide analytical expressions to evaluate the secrecy performance. We demonstrate trade-off between secrecy and reliability, and also provide an upper bound of secret information rate in finite blocklength case. The results are also applied to indicate asymptotic performance.

Cognitive radio and MIMO have drawn significant attention to achieve high spectrum utilization efficiency. On the other hand, increasing energy demand and soaring energy related operating cost call for new design of energy efficient communication networks. In this paper, we study the energy efficiency optimization in a cognitive radio MIMO network. Specifically, we propose both distributed and centralized energy efficiency optimization algorithms. Since the original fractional problems are nonconvex, we use Dinkelbach's method to transform them into parametric problem and solve the optimal solution iteratively. Simulation results show that, while the centralized algorithm outperforms the distributed algorithm in terms of network-wise energy efficiency, it may lose fairness among all CR links in high interference scenario.