mlsanityData to 14 Oct 2026

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

arXiv 2509.03709 ↗First posted 3 Sep 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 67 references that are in the map. First posted on arXiv 3 Sep 2025.

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34 of 67 references.

References · 34

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  3. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  6. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  7. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  8. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  9. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  10. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 117
  11. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 84
  12. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
22 more
  1. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 64
  2. 2020Fast-Convergent Federated Learningcited by 61
  3. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  4. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  5. 2020Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networkscited by 42
  6. 2023Graph Signal Processing: History, Development, Impact, and Outlookcited by 39
  7. 2019Asynchronous Decentralized SGD with Quantized and Local Updatescited by 36
  8. 2022Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networkscited by 32
  9. 2022Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Datacited by 19
  10. 2019Gossip-based Actor-Learner Architectures for Deep Reinforcement Learningcited by 16
  11. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  12. 2023FedBone: Towards Large-Scale Federated Multi-Task Learningcited by 14
  13. 2023Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networkscited by 13
  14. 2016Centrality Measures in Networkscited by 11
  15. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 8
  16. 2014Spreading processes in Multilayer Networkscited by 7
  17. 2024Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalitiescited by 6
  18. 2019Interacting spreading processes in multilayer networkscited by 4
  19. 2024Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selectioncited by 3
  20. 2018An Analysis of Centrality Measures for Complex and Social Networkscited by 2
  21. 2013Asynchronous Gossip-Based Random Projection Algorithms Over Networkscited by 1
  22. 2018Infection Analysis on Irregular Networks through Graph Signal Processingcited by 1

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