  In cellular federated edge learning (FEEL), multiple edge devices holding
local data jointly train a neural network by communicating learning updates
with an access point without exchanging their data samples. With very limited
communication resources, it is beneficial to schedule the most informative
local learning updates. In this paper, a novel scheduling policy is proposed to
exploit both diversity in multiuser channels and diversity in the "importance"
of the edge devices' learning updates. First, a new probabilistic scheduling
framework is developed to yield unbiased update aggregation in FEEL. The
importance of a local learning update is measured by its gradient divergence.
If one edge device is scheduled in each communication round, the scheduling
policy is derived in closed form to achieve the optimal trade-off between
channel quality and update importance. The probabilistic scheduling framework
is then extended to allow scheduling multiple edge devices in each
communication round. Numerical results obtained using popular models and
learning datasets demonstrate that the proposed scheduling policy can achieve
faster model convergence and higher learning accuracy than conventional
scheduling policies that only exploit a single type of diversity.
