From federated learning to X-Learning: breaking the barriers of decentrality through random walks

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