  With the rapid growth in mobile computing, massive amounts of data and
computing resources are now located at the edge. To this end, Federated
learning (FL) is becoming a widely adopted distributed machine learning (ML)
paradigm, which aims to harness this expanding skewed data locally in order to
develop rich and informative models. In centralized FL, a collection of devices
collaboratively solve a ML task under the coordination of a central server.
However, existing FL frameworks make an over-simplistic assumption about
network connectivity and ignore the communication bandwidth of the different
links in the network. In this paper, we present and study a novel FL algorithm,
in which devices mostly collaborate with other devices in a pairwise manner.
Our nonparametric approach is able to exploit network topology to reduce
communication bottlenecks. We evaluate our approach on various FL benchmarks
and demonstrate that our method achieves 10X better communication efficiency
and around 8% increase in accuracy compared to the centralized approach.
