Federated and Fog Learning:

Federated learning has generated significant interest, with nearly all works focused on a "star" topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through the network dimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra- and inter-layer model learning that considers the network as a multi-layer cluster-based structure, each layer of which consists of multiple device clusters. MH-FL considers the topology structures among the nodes in the clusters, including local networks formed via device-to-device (D2D) communications. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to form local consensus on the model parameters, and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy.