  Federated Learning (FL) is a well-known framework for successfully performing
a learning task in an edge computing scenario where the devices involved have
limited resources and incomplete data representation. The basic assumption of
FL is that the devices communicate directly or indirectly with a parameter
server that centrally coordinates the whole process, overcoming several
challenges associated with it. However, in highly pervasive edge scenarios, the
presence of a central controller that oversees the process cannot always be
guaranteed, and the interactions (i.e., the connectivity graph) between devices
might not be predetermined, resulting in a complex network structure. Moreover,
the heterogeneity of data and devices further complicates the learning process.
This poses new challenges from a learning standpoint that we address by
proposing a communication-efficient Decentralised Federated Learning (DFL)
algorithm able to cope with them. Our solution allows devices communicating
only with their direct neighbours to train an accurate model, overcoming the
heterogeneity induced by data and different training histories. Our results
show that the resulting local models generalise better than those trained with
competing approaches, and do so in a more communication-efficient way.
