Edge Cloud Collaborative

Edge cloud collaborative systems aim to optimize the deployment and execution of AI models, particularly large generative models, by intelligently distributing workloads between resource-constrained edge devices and powerful cloud servers. Current research focuses on developing efficient algorithms, such as multi-agent reinforcement learning and layer-wise federated learning, to manage resource allocation, minimize latency, and adapt to dynamic environments, often employing model decomposition and knowledge distillation techniques. This approach promises significant improvements in performance and efficiency for various applications, including real-time video analytics, industrial IoT, and personalized AI services, while addressing challenges like data privacy and bandwidth limitations.