What question did this study set out to answer?

This work aims to introduce and explore the concept of $${ extbf{X}}$$-Learning, a decentralized learning architecture.

May 6, 2026Open Access

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

Key Points

This work aims to introduce and explore the concept of $${ extbf{X}}$$-Learning, a decentralized learning architecture.
Discussed design considerations and degrees of freedom in $${ extbf{X}}$$-Learning.
Explored connections between $${ extbf{X}}$$-Learning, graph theory, and Markov chains.
Outlined open research directions for further investigation.
Established relationships between decentralized concepts and graph theory.
Highlighted the role of Markov chains in understanding $${ extbf{X}}$$-Learning.

Abstract

Abstract 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.

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Allan Salihovic

Payam Abdisarabshali

Michael Langberg

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

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Abstract

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