A Graph Network Model For Distributed Learning With Limited Bandwidth Links And Privacy Constraints

In this work, we develop an algorithm based on graph networks to train distributedly a deep learning model. We consider that there are several nodes, in an arbitrary network topology, each one of them having access to a local dataset that, for privacy concerns, cannot be shared with other nodes. We also assume that there are bandwidth constraints, and hence, it may not be affordable sharing the weights of the model with other nodes. Our algorithm makes use of pruning and an autoencoder to train under all these constraints, and our simulations show that it provides a good accuracy, while preserving the privacy of the data and providing a compression of nearly two orders of magnitude in the transmitted bits.