Edge Cloud Collaboration

Edge cloud collaboration optimizes AI model deployment by strategically distributing computational tasks between resource-constrained edge devices and powerful cloud servers. Current research emphasizes efficient task allocation using reinforcement learning, knowledge transfer techniques (like guidance-based methods), and adaptive strategies to handle dynamic data and model variations, often incorporating large language models or large vision models. This approach improves real-time performance, reduces latency and energy consumption, and enhances the capabilities of AI systems in resource-limited environments like autonomous vehicles and IoT applications, impacting both scientific understanding of distributed AI and practical deployment in various fields.