As one of the typical application scenarios in the fifth generation (5G) mobile communication system, the situation of high-speed mobile communication is receiving increasing attention. The railway tunnel is a typical environment for high-speed mobile communications. Railway tunnels for high-speed trains are generally installed with leaky coaxial cables (LCXs), which can radiate and receive electromagnetic waves through slots; thus, providing communication. To evaluate the system-level performance of the LCX channel in a tunnel, we propose a modified geometrically based single-bounce multiple-input-multiple-output (GBSB-MIMO) channel model considering the effect of Doppler spread. The time-domain statistics of the channel model are studied from numerical simulations. Based on the proposed channel model, we also simulate and analyze the effects of different factors on the 5G system-level performance, including the interval of cable slots and the quantity and location of LCX.

As a popular distributed learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security and enjoying benefits in processing large-scale, high-dimensional data. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Local differential privacy (LDP) has recently worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel privacy-enhanced federated learning framework (Optimal LDP-FL) which achieves local differential privacy protection by the client self-sampling and data perturbation mechanisms. We theoretically analyze the relationship between the model accuracy and client self-sampling probability. Restrictive client self-sampling technology is proposed which eliminates the randomness of the self-sampling probability settings in existing studies and improves the utilization of the federated system. A novel, efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony) is also proposed, which allows an adaptive parameter range to reduce variance and improve model accuracy. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.

To accelerate the transformation of economic growth mode and promote high-quality economic development, it is necessary to clarify the specific impact of population aging on consumption expenditure and the differences between urban and rural areas. This paper selects chip2013 panel data as samples to construct a regression model and uses OB decomposition to analyze. The results show that the aging of the population has a significant negative impact on consumption expenditure, but the difference between urban and rural consumption is mainly due to their own endowment and consumption concept, rather than the degree of aging. The aging of the population is a long-term trend of China's population development. The research results will help us adjust policies to resist the adverse effects of aging on economic development.

Federated learning (FL) pours vitality into developing data-driven AI. However, there are still some challenges, such as balancing the security and efficiency in FL. Differential privacy is one of the dominant means in privacy-preserving machine learning. Local differential privacy (LDP) further realizes the confidentiality of the server by perturbing the transmitting parameters, which is naturally applicable for the decentralized FL. However, the current research exists the weaknesses of low communication efficiency and poor adaptability in complex deep learning models. In this work, we propose an efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony), which allows adaptive parameter range to reduce variance and improve model accuracy. Specifically, each client in each round adaptively selects perturbation parameters according to model training. Furthermore, only 1-bit data transmission for each dimension of the model parameters, thus significantly reducing the communication overhead. Theoretical analysis and proof have shown that Adaptive-Harmony holds the same asymptotic error bounds and convergence performance as advanced works but with minimal communication costs. An LDP-FL framework (Optimal LDP-FL) is also proposed, taking Adaptive-Harmony as the core. We also introduce a parameter shuffling in the Optimal LDP-FL, which avoids server tracking clients through the model parameters, thereby improving privacy levels without consuming the privacy budget. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.