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

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 processing. While 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 feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by optimizing transmission power control, data migration, and computing resource allocation. It 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 violations. Simulation results indicate that the proposed algorithm effectively reduces data processing latency, energy consumption, and convergence time.