{"ID":2875179,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03709","arxiv_id":"2509.03709","title":"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.","short_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-triv...","url_abs":"https://arxiv.org/abs/2509.03709","url_pdf":"https://arxiv.org/pdf/2509.03709v3","authors":"[\"Allan Salihovic\",\"Payam Abdisarabshali\",\"Michael Langberg\",\"Seyyedali Hosseinalipour\"]","published":"2025-09-03T20:48:41Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}