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

Allan Salihovic; Payam Abdisarabshali; Michael Langberg; Seyyedali Hosseinalipour

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

Machine Learning 2025-10-13 v3 Artificial Intelligence

Authors: Allan Salihovic , Payam Abdisarabshali , Michael Langberg , Seyyedali Hosseinalipour

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.

Keywords

Federated Learning X-Learning Decentralization Graph Theory Markov Chains

Cite

@article{arxiv.2509.03709,
  title  = {From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks},
  author = {Allan Salihovic and Payam Abdisarabshali and Michael Langberg and Seyyedali Hosseinalipour},
  journal= {arXiv preprint arXiv:2509.03709},
  year   = {2025}
}

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

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