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

• URL: http://arxiv.org/abs/2512.24694v1
• Date: Wed, 31 Dec 2025 07:59:29 GMT
• Title: Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach
• Authors: Reza Jahani, Md Farhamdur Reza, Richeng Jin, Huaiyu Dai,
• Abstract summary: 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.<n>We systematically investigate the role of mobility in improving DFL performance.<n>We propose a DFL framework that utilizes mobile users with induced mobility patterns, allowing them to exploit the knowledge of data distribution to enhance information propagation through the network.
• Score: 32.667262494165534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Distribution-Aware Mobility-Assisted Decentralized Federated Learning [28.986426579981924]
  Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server.<n>We show that introducing a small fraction of mobile clients, even with random movement, can significantly improve the accuracy of DFL by facilitating information flow.<n>We propose novel distribution-aware mobility patterns, where mobile clients strategically navigate the network.
  arXiv  Detail & Related papers  (2025-05-24T20:47:42Z)
• Performance Analysis of Decentralized Federated Learning Deployments [1.7249361224827533]
  Decentralized Federated Learning (DFL) is introduced to address these challenges.<n>It facilitates direct collaboration among participating devices without relying on a central server.<n>This work explores crucial factors influencing the convergence and generalization capacity of DFL models.
  arXiv  Detail & Related papers  (2025-03-14T19:37:13Z)
• Personalized Wireless Federated Learning for Large Language Models [75.22457544349668]
  Large language models (LLMs) have driven profound transformations in wireless networks.<n>Within wireless environments, the training of LLMs faces significant challenges related to security and privacy.<n>This paper presents a systematic analysis of the training stages of LLMs in wireless networks, including pre-training, instruction tuning, and alignment tuning.
  arXiv  Detail & Related papers  (2024-04-20T02:30:21Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Data-Heterogeneous Hierarchical Federated Learning with Mobility [20.482704508355905]
  Federated learning enables distributed training of machine learning (ML) models across multiple devices. We consider a data-heterogeneous HFL scenario with mobility, mainly targeting vehicular networks. We show that mobility can indeed improve the model accuracy by up to 15.1% when training a convolutional neural network.
  arXiv  Detail & Related papers  (2023-06-19T04:22:18Z)
• Spatio-Temporal Federated Learning for Massive Wireless Edge Networks [23.389249751372393]
  An edge server and numerous mobile devices (clients) jointly learn a global model without transporting huge amount of data collected by the mobile devices to the edge server. The proposed FL approach exploits spatial and temporal correlations between learning updates from different mobile devices scheduled to join STFL in various trainings. An analytical framework of STFL is proposed and employed to study the learning capability of STFL via its convergence performance.
  arXiv  Detail & Related papers  (2021-10-27T16:46:45Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks [81.83990083088345]
  We develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks. Our analysis proves that the learning performance of HFL deteriorates drastically with highly-mobile users. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm.
  arXiv  Detail & Related papers  (2021-08-20T10:46:58Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)
• Communication Efficient Federated Learning with Energy Awareness over Wireless Networks [51.645564534597625]
  In federated learning (FL), the parameter server and the mobile devices share the training parameters over wireless links. We adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Two optimization problems are formulated and solved, which optimize the learning performance. Considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a sign-based algorithm is proposed.
  arXiv  Detail & Related papers  (2020-04-15T21:25:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.