Collaborative Optimization of Wireless Communication and Computing Resource Allocation based on Multi-Agent Federated Weighting Deep Reinforcement Learning

As artificial intelligence (AI)-enabled wireless communication systems continue their evolution, distributed learning has gained widespread attention for its ability to offer enhanced data privacy protection, improved resource utilization, and enhanced fault tolerance within wireless communication applications. Federated learning further enhances the ability of resource coordination and model generalization across nodes based on the above foundation, enabling the realization of an AI-driven communication and computing integrated wireless network. This paper proposes a novel wireless communication system to cater to a personalized service needs of both privacy-sensitive and privacy-insensitive users. We design the system based on based on multi-agent federated weighting deep reinforcement learning (MAFWDRL). The system, while fulfilling service requirements for users, facilitates real-time optimization of local communication resources allocation and concurrent decision-making concerning computing resources. Additionally, exploration noise is incorporated to enhance the exploration process of off-policy deep reinforcement learning (DRL) for wireless channels. Federated weighting (FedWgt) effectively compensates for heterogeneous differences in channel status between communication nodes. Extensive simulation experiments demonstrate that the proposed scheme outperforms baseline methods significantly in terms of throughput, calculation latency, and energy consumption improvement

Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method

Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations

REVIEW ON SUB-SYNCHRONOUS OSCILLATIONS IN WIND FARMS: ANALYSIS METHOD, STUDY SYSTEM, AND DAMPING CONTROL

More and more attention on wind farm sub-synchronous oscillation (SSO) has been paid as many SSO incidents in wind farms have occurred. This paper presents an overview of recent SSO issues in wind farm from the perspective of control, including the analysis methods, the study system, and the SSO mitigation by damping control. Three major analysis methods, as well as different study systems for wind farm SSO study, are comprehensively reviewed. The adaptability and complexity of the methods and study systems are analysed, and an overall survey of recent SSO analysis is given. Among the wind farm SSO mitigation methods, the sub-synchronous damping controller (SSDC) is one of the most commonly used methods in practice. Its configuration and signal selection are introduced in this paper

Difference-based Deep Convolutional Neural Network for Simulation-to-reality UAV Fault Diagnosis

Identifying the fault in propellers is important to keep quadrotors operating safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault diagnosis methods provide a cost-effective and safe approach to detect the propeller faults. However, due to the gap between simulation and reality, classifiers trained with simulated data usually underperform in real flights. In this work, a new deep neural network (DNN) model is presented to address the above issue. It uses the difference features extracted by deep convolutional neural networks (DDCNN) to reduce the sim-to-real gap. Moreover, a new domain adaptation method is presented to further bring the distribution of the real-flight data closer to that of the simulation data. The experimental results show that the proposed approach can achieve an accuracy of 97.9\% in detecting propeller faults in real flight. Feature visualization was performed to help better understand our DDCNN model.Comment: 7 pages, 8 figure

Environmental efficiency analysis of listed cement enterprises in China

© 2016 by the authors.China's cement production has been the highest worldwide for decades and contributes significant environmental pollution. Using a non-radical DEA model with slacks-based measure (SBM), this paper analyzes the environmental efficiency of China's listed cement companies. The results suggest that the average mean of the environmental efficiency for the listed cement enterprises shows a decreasing trend in 2012 and 2013. There is a significant imbalance in environmental efficiency in these firms ranging from very low to very high. Further investigation finds that enterprise size and property structure are key factors. Increasing production concentration and decreasing the share of government investment could improve the environmental efficiency. The findings also suggest that effectively monitoring pollution products can improve environmental efficiency quickly, whereas pursuit for excessive profitability without keeping the same pace in energy saving would cause a sharp drop in environmental efficiency. Based on these findings, we proposed that companies in the Chinese cement sector might consider restructuring to improve environmental efficiency. They also need to make a trade-off between profitability and environmental protection. Finally, the Chinese government should reduce ownership control and management interventions in cement companies

GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions

Recently, impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However, current diffusion models primarily generate images by predicting noise in the latent space, and the editing is usually applied to the whole image, which makes it challenging to perform delicate, especially localized, editing for 3D scenes. Inspired by recent 3D Gaussian splatting, we propose a systematic framework, named GaussianEditor, to edit 3D scenes delicately via 3D Gaussians with text instructions. Benefiting from the explicit property of 3D Gaussians, we design a series of techniques to achieve delicate editing. Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians. The Gaussian RoI is further used to control the editing process. Our framework can achieve more delicate and precise editing of 3D scenes than previous methods while enjoying much faster training speed, i.e. within 20 minutes on a single V100 GPU, more than twice as fast as Instruct-NeRF2NeRF (45 minutes -- 2 hours).Comment: Project page: https://GaussianEditor.github.i