Optimizing Resource Management for Efficient Two-Stage Edge Learning

The core message of this paper is to propose a joint communication and computation resource management design to optimize the performance of a two-stage edge learning system, which consists of a model pre-training stage at the edge server and a task-specific fine-tuning stage via federated edge learning at the edge devices.