  With growth in the number of smart devices and advancements in their
hardware, in recent years, data-driven machine learning techniques have drawn
significant attention. However, due to privacy and communication issues, it is
not possible to collect this data at a centralized location. Federated learning
is a machine learning setting where the centralized location trains a learning
model over remote devices. Federated learning algorithms cannot be employed in
the real world scenarios unless they consider unreliable and
resource-constrained nature of the wireless medium. In this paper, we propose a
federated learning algorithm that is suitable for cellular wireless networks.
We prove its convergence, and provide the optimal scheduling policy that
maximizes the convergence rate. We also study the effect of local computation
steps and communication steps on the convergence of the proposed algorithm. We
prove, in practice, federated learning algorithms may solve a different problem
than the one that they have been employed for if the unreliability of wireless
channels is neglected. Finally, through numerous experiments on real and
synthetic datasets, we demonstrate the convergence of our proposed algorithm.
