Unifying AI for Networking and Networking for AI: the Self-evolving Edge Learning

Edge Learning environments, characterized by limited wireless resources, encounter significant bottlenecks in network performance, particularly in Federated Learning (FL) tasks. Current resource allocation strategies are primarily classified into “AI for Networking” and “Networking for AI”. However, both approaches fail to adequately address the interaction between network states and AI task requirements, thereby limiting their overall effectiveness. To address this, we propose a novel bidirectional dynamic collaborative optimization mechanism that enables real-time interaction between AI task performance and network states. This mechanism adjusts both AI task resource requirements and network configurations based on performance feedback, breaking away from traditional unidirectional optimization approaches. We introduce the AI-network unified algorithm, which incorporates data-driven dynamic sensing and enhances system adaptability and robustness, achieving self-optimization in edge learning. Theoretical analysis and simulation results demonstrate the significant advantages of our approach in simultaneously improving network resource utilization and AI task performance, providing an effective solution for the future wireless network.