  Federated learning (FL) allows predictive model training on the sensed data
in a wireless Internet of things (IoT) network evading data collection cost in
terms of energy, time, and privacy. In this paper, for a FL setting, we model
the learning gain achieved by an IoT device against its participation cost as
its utility. The local model quality and the associated cost differs from
device to device due to the device-heterogeneity which could be time-varying.
We identify that this results in utility unfairness because the same global
model is shared among the devices. In the vanilla FL setting, the master is
unaware of devices' local model computation and transmission costs, thus it is
unable to address the utility unfairness problem. In addition, a device may
exploit this lack of knowledge at the master to intentionally reduce its
expenditure and thereby boost its utility. We propose to control the quality of
the global model shared with the devices, in each round, based on their
contribution and expenditure. This is achieved by employing differential
privacy to curtail global model divulgence based on the learning contribution.
Furthermore, we devise adaptive computation and transmission policies for each
device to control its expenditure in order to mitigate utility unfairness. Our
results show that the proposed scheme reduces the standard deviation of the
energy cost of devices by 99% in comparison to the benchmark scheme, while the
standard deviation of the training loss of devices varies around 0.103.
