  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.
