Multi-Stage Hybrid Federated Learning Over Large-Scale D2D-Enabled Fog Networks
Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolò Michelusi, Vaneet Aggarwal, David J. Love, Huaiyu Dai · IEEE/ACM Transactions on Networking · 2022
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 thenetworkdimension 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 amulti-layer cluster-based structure.MH-FLconsiders thetopology structuresamong the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes asemi-decentralized architecturefor federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to formlocal consensuson the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence forMH-FLwith respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm forMH-FLto tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages ofMH-FLin terms of resource utilization metrics.