  The popularity of mobile devices results in the availability of enormous data
and computational resources at the network edge. To leverage the data and
resources, a new machine learning paradigm, called edge learning, has emerged
where learning algorithms are deployed at the edge for providing fast and
intelligent services to mobile users. While computing speeds are advancing
rapidly, the communication latency is becoming the bottleneck of fast edge
learning. To address this issue, this work is focused on designing a low
latency multi-access scheme for edge learning. We consider a popular framework,
federated edge learning (FEEL), where edge-server and on-device learning are
synchronized to train a model without violating user-data privacy. It is
proposed that model updates simultaneously transmitted by devices over
broadband channels should be analog aggregated "over-the-air" by exploiting the
superposition property of a multi-access channel. Thereby, "interference" is
harnessed to provide fast implementation of the model aggregation. This results
in dramatical latency reduction compared with the traditional orthogonal access
(i.e., OFDMA). In this work, the performance of FEEL is characterized targeting
a single-cell random network. First, due to power alignment between devices as
required for aggregation, a fundamental tradeoff is shown to exist between the
update-reliability and the expected update-truncation ratio. This motivates the
design of an opportunistic scheduling scheme for FEEL that selects devices
within a distance threshold. This scheme is shown using real datasets to yield
satisfactory learning performance in the presence of high mobility. Second,
both the multi-access latency of the proposed analog aggregation and the OFDMA
scheme are analyzed. Their ratio, which quantifies the latency reduction of the
former, is proved to scale almost linearly with device population.
