  Federated Learning (FL) is a distributed machine learning technique, where
each device contributes to the learning model by independently computing the
gradient based on its local training data. It has recently become a hot
research topic, as it promises several benefits related to data privacy and
scalability. However, implementing FL at the network edge is challenging due to
system and data heterogeneity and resources constraints. In this article, we
examine the existing challenges and trade-offs in Federated Edge Learning
(FEEL). The design of FEEL algorithms for resources-efficient learning raises
several challenges. These challenges are essentially related to the
multidisciplinary nature of the problem. As the data is the key component of
the learning, this article advocates a new set of considerations for data
characteristics in wireless scheduling algorithms in FEEL. Hence, we propose a
general framework for the data-aware scheduling as a guideline for future
research directions. We also discuss the main axes and requirements for data
evaluation and some exploitable techniques and metrics.
