From Federated Learning to $mathbb{X}$-Learning: Breaking the Barriers of Decentrality Through Random Walks

arXiv:2509.03709v1 Announce Type: new Abstract: We provide our perspective on $mathbb{X}$-Learning ($mathbb{X}$L), a novel distributed learning architecture that generalizes and extends the concept of decentralization. Our goal is to present a vision for $mathbb{X}$L, introducing its unexplored design considerations and degrees of freedom. To this end, we shed light on the intuitive yet non-trivial connections between $mathbb{X}$L, graph theory, and Markov chains. We also present a series of open research directions to stimulate further research.

2025-09-05 04:30 GMT · 7 months ago arxiv.org

arXiv:2509.03709v1 Announce Type: new Abstract: We provide our perspective on $mathbb{X}$-Learning ($mathbb{X}$L), a novel distributed learning architecture that generalizes and extends the concept of decentralization. Our goal is to present a vision for $mathbb{X}$L, introducing its unexplored design considerations and degrees of freedom. To this end, we shed light on the intuitive yet non-trivial connections between $mathbb{X}$L, graph theory, and Markov chains. We also present a series of open research directions to stimulate further research.

Original: https://arxiv.org/abs/2509.03709