  Federated learning (FL) promotes predictive model training at the Internet of
things (IoT) devices by evading data collection cost in terms of energy, time,
and privacy. We model the learning gain achieved by an IoT device against its
participation cost as its utility. Due to the device-heterogeneity, the local
model learning cost and its quality, which can be time-varying, differs from
device to device. We show that this variation results in utility unfairness
because the same global model is shared among the devices. By default, the
master is unaware of the local model computation and transmission costs of the
devices, thus it is unable to address the utility unfairness problem. Also, a
device may exploit this lack of knowledge at the master to intentionally reduce
its expenditure and thereby enhance 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.
In addition, 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.
