Robust machine learning approaches to wireless communication networks

Artificial intelligence is a key enabler of sixth-generation wireless systems. In this thesis, we target fundamental problems arising from the interaction between these two technologies, with the end goal of paving the way towards reliable AI in future networks. We first develop distributed training algorithms that can be deployed at the edge of wireless networks despite the communication bottlenecks, unreliability of its workers and data heterogeneity. We then take a critical look at the application of the standard frequentist learning paradigm to wireless communication problems. We propose an extension of the generalized Bayesian learning, that concurrently counteracts three fundamental challenges arising in this domain: data scarcity, outliers and model misspecification.