  Federated learning is a semi-distributed algorithm, where a server
communicates with multiple dispersed clients to learn a global model. The
federated architecture is not robust and is sensitive to communication and
computational overloads due to its one-master multi-client structure. It can
also be subject to privacy attacks targeting personal information on the
communication links. In this work, we introduce graph federated learning (GFL),
which consists of multiple federated units connected by a graph. We then show
how graph homomorphic perturbations can be used to ensure the algorithm is
differentially private. We conduct both convergence and privacy theoretical
analyses and illustrate performance by means of computer simulations.
