Distributed Artificial Intelligence Algorithm Design in Edge Computing Environment

This article discusses creating distributed AI algorithms for edge computing and suggests a model optimization framework using federated learning to boost computing efficiency and real-time system performance. In common smart grid use cases, edge nodes can effectively handle large amounts of real-time data by utilizing distributed architecture, decreasing communication delays with local model updates, and improving the system's performance as a whole. This paper compares the performance differences between distributed and centralized models in experiments. The results show that the distributed model performs well in terms of convergence speed, communication delay and system response time, and has stronger scalability and adaptability. This algorithm can not only effectively reduce network load, but also ensure data privacy and security, providing theoretical support and application examples for the design of distributed artificial intelligence algorithms in edge computing environments.