A Distributed Hierarchical Deep Computation Model for Federated Learning in Edge Computing

Abstract

Deep learning has recently garnered significant interest in many applications especially for big data analytics in the edge computing environment. Federated learning, as a novel machine learning technique, aims to build a shared learning model from training data on distributed edge nodes to protect data privacy. However, the model update in federated learning requires parameter exchanges among edge nodes, which is rather bandwidth-consuming. This article proposes a novel distributed hierarchical tensor deep computation model by condensing the model parameters from a high-dimensional tensor space into a set of low-dimensional subspaces to reduce the bandwidth consumption and storage requirement for federated learning. Moreover, an updating approach with a hierarchical tensor back-propagation algorithm is developed by directly computing the gradients of low-dimensional parameters to reduce the memory requirement of training for edge nodes and improve training efficiency. Finally, extensive simulations on classical datasets with different local data distributions are presented for the performance evaluation. The results demonstrate that the proposed model relieves the burden of communication bandwidth and reduces energy consumption at edge nodes for federated learning.