From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks

Abstract

We provide our perspective on X-Learning (XL), a novel distributed learning architecture that generalizes and extends the concept of decentralization. Our goal is to present a vision for XL, introducing its unexplored design considerations and degrees of freedom. To this end, we shed light on the intuitive yet non-trivial connections between XL, graph theory, and Markov chains. We also present a series of open research directions to stimulate further research.