  Machine learning (ML) tasks are becoming ubiquitous in today's network
applications. Federated learning has emerged recently as a technique for
training ML models at the network edge by leveraging processing capabilities
across the nodes that collect the data. There are several challenges with
employing conventional federated learning in contemporary networks, due to the
significant heterogeneity in compute and communication capabilities that exist
across devices. To address this, we advocate a new learning paradigm called fog
learning which will intelligently distribute ML model training across the
continuum of nodes from edge devices to cloud servers. Fog learning enhances
federated learning along three major dimensions: network, heterogeneity, and
proximity. It considers a multi-layer hybrid learning framework consisting of
heterogeneous devices with various proximities. It accounts for the topology
structures of the local networks among the heterogeneous nodes at each network
layer, orchestrating them for collaborative/cooperative learning through
device-to-device (D2D) communications. This migrates from star network
topologies used for parameter transfers in federated learning to more
distributed topologies at scale. We discuss several open research directions to
realizing fog learning.
