With the development of the technologies deployed on vehicles, there is a significant increase in the amount of data, which comes from various applications, such as battery management, VR, autopilot, etc. However, privacy is a critical obstacle to utilizing such information since many vehicle-based applications involve locations, conversations, driving behaviors, etc. Federated Learning (FL) is a promising technology perfect for filling the gap, as it keeps users' data in their devices, which gives rise to Federated Vehicular Networks (FVN). As a distributed paradigm, communication efficiency is an essential issue for researchers, since it impacts every aspect of FVN. This paper presents a communication-efficient federated vehicular network called FedAGL integrating Adaptive parameter control, Genetic Algorithm (GA), and Long Short-Term Memory (LSTM). This is a framework designed to accelerate the training process of FVN by minimizing communication overhead and latency. In this paper, we introduce a parameter-control algorithm to alleviate transmission burdens, thereby reducing communication overhead. This approach does not involve freezing the parameters of the entire layer but identifies certain parameters with minor updates. Further, we employ the GA to allocate communication bandwidth for each vehicle, considering the varying sizes of vehicles' active parameters. Thus, the maximum transmission time in each FL round decreases. In addition, due to the complexity of the genetic algorithm, the LSTM model is developed to accelerate the bandwidth allocation with quick response and sufficient precision, which uses data from the genetic algorithm as training labels. Abundant experiments indicate that the proposed framework achieves up to 10.44% and 22.62% reduction in communication overhead and latency, respectively, outperforming the benchmarks.

Telematics technology development offers vehicles a range of intelligent and convenient functions, including navigation and mapping services, intelligent driving assistance, and intelligent traffic management. However, since these functions deal with sensitive information like vehicle location and driving habits, it is crucial to address concerns regarding information security and privacy protection. Federated learning (FL) is highly suitable for addressing such problems due to its characteristics, in which a client does not need to share private data and upload model parameters to a parameter server (PS) via the network. This results in the establishment of a federated vehicle network (FVN). As a distributed paradigm, the efficiency of communication is crucial in FL as it impacts all aspects of the FVN. This article introduces a parameter freezing algorithm based on historical information to reduce the data transferred between the client and the PS in each round of communication, thus minimizing the communication overhead of FL. Additionally, we propose using a particle swarm algorithm to allocate network bandwidth to each vehicle based on the packet sizes sent by each vehicle (i.e., the nonfreezing parameters) to minimize the communication latency in each FL round. Furthermore, due to the high time complexity of the particle swarm algorithm, we employ it to generate training data for training a transformer model with fast response and sufficient accuracy, thereby accelerating the bandwidth allocation process. Through extensive experiments, we prove the feasibility of our approach and its efficiency in improving communication in FL.

The secure sharing of data is crucial for peer-to-peer energy trading. However, the vulnerability of Information and Communication Technology (ICT) infrastructures to cyberattacks, e.g., Denial of Service (DoS) attacks, poses a significant challenge. A possible solution is to use Digital Twin (DT) modeling of the physical system, which provides robust digital mapping and Big Data processing capabilities that facilitate data recovery. To this end, this paper proposes a DT-enabled energy trading framework for cyber-physical energy systems that offers data analytic and recovery capabilities to defend from DoS attacks. With this framework, a new distributed approximate-newton trading algorithm with a switched triggering control strategy is proposed. Therein, the DT model is employed to achieve data recovery and adjust the system evolution of trading trajectory during attack periods. This enables the proposed method to find optimal trading solutions even in the presence of DoS attacks. Theoretical analysis results demonstrate the correctness of the proposed method. Furthermore, numerical simulations are conducted to assess the effectiveness of the proposed method.

This paper proposes an energy management strategy that can resist DOS attacks for solving the Economic Dispatch Problem (EDP) of the smart grid. We use the concept of energy agent, which acts as a hub for the smart grid, and each EA is an integrated energy unit that converts, stores, and utilizes its local energy resources. This approach takes into account the coupling relationship between energy agents (EA) and utilizes the Lyapunov function technique to achieve finite-time solutions for optimization problems. We incorporate strategies to resist DOS attacks when analyzing finite-time convergence using the Lyapunov technique. Based on this, a finite convergence time related to DOS attack time is derived. The integral sliding mode control strategy is adopted and the Lyapunov method is used to analyze it, so that the algorithm can resist DOS attacks and resist external disturbances. Through theoretical analysis, it is shown that the strategy is capable of converging to the global optimal solution in finite time even if it is attacked by DOS. We conducted case studies of six-EA and ten-EA systems to verify the effectiveness of this strategy. The proposed strategy has potential for deployment in distributed energy management systems that require resilience against DOS attacks.