From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
It proposes a new paradigm for distributed learning, potentially impacting researchers in decentralized AI, but is conceptual and incremental in nature.
The paper introduces X-Learning (XL) as a novel distributed learning architecture that generalizes decentralization, exploring its connections to graph theory and Markov chains, and outlines open research directions without presenting specific results or numbers.
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.