From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911200944586752 |
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| author | Salihovic, Allan Abdisarabshali, Payam Langberg, Michael Hosseinalipour, Seyyedali |
| author_facet | Salihovic, Allan Abdisarabshali, Payam Langberg, Michael Hosseinalipour, Seyyedali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks Salihovic, Allan Abdisarabshali, Payam Langberg, Michael Hosseinalipour, Seyyedali Machine Learning Artificial Intelligence 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. |
| title | From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03709 |