  Federated Edge Learning (FEEL) involves the collaborative training of machine
learning models among edge devices, with the orchestration of a server in a
wireless edge network. Due to frequent model updates, FEEL needs to be adapted
to the limited communication bandwidth, scarce energy of edge devices, and the
statistical heterogeneity of edge devices' data distributions. Therefore, a
careful scheduling of a subset of devices for training and uploading models is
necessary. In contrast to previous work in FEEL where the data aspects are
under-explored, we consider data properties at the heart of the proposed
scheduling algorithm. To this end, we propose a new scheduling scheme for
non-independent and-identically-distributed (non-IID) and unbalanced datasets
in FEEL. As the data is the key component of the learning, we propose a new set
of considerations for data characteristics in wireless scheduling algorithms in
FEEL. In fact, the data collected by the devices depends on the local
environment and usage pattern. Thus, the datasets vary in size and
distributions among the devices. In the proposed algorithm, we consider both
data and resource perspectives. In addition to minimizing the completion time
of FEEL as well as the transmission energy of the participating devices, the
algorithm prioritizes devices with rich and diverse datasets. We first define a
general framework for the data-aware scheduling and the main axes and
requirements for diversity evaluation. Then, we discuss diversity aspects and
some exploitable techniques and metrics. Next, we formulate the problem and
present our FEEL scheduling algorithm. Evaluations in different scenarios show
that our proposed FEEL scheduling algorithm can help achieve high accuracy in
few rounds with a reduced cost.
