Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

作     者：Zhan Shi 

作者机构：Electric Power Dispatching Control CenterGuangdong Power Grid Co.Ltd.Guangzhou510030China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第80卷第7期

页      面：973-994页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：supported by China Southern Power Grid Technology Project under Grant 03600KK52220019(GDKJXM20220253) 

主　　题：IoT federated learning generative adversarial network data processing multi-flowintegration energy aggregation dispatch 

摘      要：The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data *** generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making ***,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these *** proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource *** employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint *** results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.