In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms.

翻译：在物联网网络中，面向数据驱动任务的边缘学习为智能应用与服务提供了支撑。随着网络规模不断扩大，不同用户可能产生差异化的数据集。为适配大规模物联网网络中多种边缘学习任务的需求，本文通过无线资源分配与边缘学习误差预测的协同设计，基于任务导向原则实现高效通信。具体而言，我们首先通过多用户调度缓解密集网络中的同频干扰，随后针对不同学习任务并行执行最优功率分配。得益于所设计算法的高度并行化特性，大量实验结果表明，与当前最先进的基准算法相比，多用户调度与任务导向功率分配能够高效提升不同边缘学习任务的性能。