  The Internet of Things (IoT) will be ripe for the deployment of novel machine
learning algorithms for both network and application management. However, given
the presence of massively distributed and private datasets, it is challenging
to use classical centralized learning algorithms in the IoT. To overcome this
challenge, federated learning can be a promising solution that enables
on-device machine learning without the need to migrate the private end-user
data to a central cloud. In federated learning, only learning model updates are
transferred between end-devices and the aggregation server. Although federated
learning can offer better privacy preservation than centralized machine
learning, it has still privacy concerns. In this paper, first, we present the
recent advances of federated learning towards enabling federated
learning-powered IoT applications. A set of metrics such as sparsification,
robustness, quantization, scalability, security, and privacy, is delineated in
order to rigorously evaluate the recent advances. Second, we devise a taxonomy
for federated learning over IoT networks. Third, we propose two IoT use cases
of dispersed federated learning that can offer better privacy preservation than
federated learning. Finally, we present several open research challenges with
their possible solutions.
