  Recent years have witnessed a rapid proliferation of smart Internet of Things
(IoT) devices. IoT devices with intelligence require the use of effective
machine learning paradigms. Federated learning can be a promising solution for
enabling IoT-based smart applications. In this paper, we present the primary
design aspects for enabling federated learning at network edge. We model the
incentive-based interaction between a global server and participating devices
for federated learning via a Stackelberg game to motivate the participation of
the devices in the federated learning process. We present several open research
challenges with their possible solutions. Finally, we provide an outlook on
future research.
