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Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach

arXiv 2512.24694 ↗First posted 31 Dec 2025cs.LGcited by 0

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Abstract (arXiv)

Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central server. However, its performance often degrades significantly due to limited connectivity and data heterogeneity. As we move toward the next generation of wireless networks, mobility is increasingly embedded in many real-world applications. The user mobility, either natural or induced, enables clients to act as relays or bridges, thus enhancing information flow in sparse networks; however, its impact on DFL has been largely overlooked despite its potential. In this work, we systematically investigate the role of mobility in improving DFL performance. We first establish the convergence of DFL in sparse networks under user mobility and theoretically demonstrate that even random movement of a fraction of users can significantly boost performance. Building upon this insight, we propose a DFL framework that utilizes mobile users with induced mobility patterns, allowing them to exploit the knowledge of data distribution to determine their trajectories to enhance information propagation through the network. Through extensive experiments, we empirically confirm our theoretical findings, validate the superiority of our approach over baselines, and provide a comprehensive analysis of how various network parameters influence DFL performance in mobile networks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 51 references that are in the map. First posted on arXiv 31 Dec 2025.

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27 of 51 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2021Model-Contrastive Federated Learningcited by 762
  7. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  8. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  9. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  10. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  11. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  12. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
15 more
  1. 2013Distributed optimization over time-varying directed graphscited by 77
  2. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 66
  3. 2022Topology-aware Generalization of Decentralized SGDcited by 37
  4. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  5. 2020Stochastic-Sign SGD for Federated Learning with Theoretical Guaranteescited by 34
  6. 2024An Aggregation-Free Federated Learning for Tackling Data Heterogeneitycited by 33
  7. 2022Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Datacited by 31
  8. 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
  9. 2021D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learningcited by 12
  10. 2022Data-heterogeneity-aware Mixing for Decentralized Learningcited by 12
  11. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  12. 2024Decentralized Federated Learning with Model Caching on Mobile Agentscited by 4
  13. 2023Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networkscited by 2
  14. 2024TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Datacited by 2
  15. 2025Distribution-Aware Mobility-Assisted Decentralized Federated Learningcited by 1

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