mlsanityData to 14 Oct 2026

Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks

arXiv 2108.09103 ↗First posted 20 Aug 2021cs.LG cs.NI cs.SYcited by 35

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Abstract (arXiv)

Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the learning performance. To fill this research gap, firstly, we develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks where the mobile users may roam across multiple edge access points, leading to incompletion of inconsistent FL training. Secondly, we provide the convergence analysis of HFL with user mobility. Our analysis proves that the learning performance of HFL deteriorates drastically with highly-mobile users. And this decline in the learning performance will be exacerbated with small number of participants and large data distribution divergences among local data of users. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm by redesigning the access mechanism, local update rule and model aggregation scheme. Finally, we provide experiments to evaluate the learning performance of HFL and our MACFL. The results show that our MACFL can enhance the learning performance, especially for three different cases, namely, the case of users with non-independent and identical distribution data, the case of users with high mobility, and the cases with a small number of users.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 28 of its 37 references that are in the map. First posted on arXiv 20 Aug 2021.

Builds on

28 of 37 references.

References · 28

  1. 2017Graph Attention Networkscited by 8,923
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  6. 2018Federated Learning with Non-IID Datacited by 1,108
  7. 2017Federated Multi-Task Learningcited by 672
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  10. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  11. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  12. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
16 more
  1. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  2. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  3. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  4. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  5. 2015Asynchronous Parallel Stochastic Gradient for Nonconvex Optimizationcited by 116
  6. 2018Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithmscited by 106
  7. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  8. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  9. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  10. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  11. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  12. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  13. 2020FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cyclingcited by 27
  14. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  15. 2020Multi-Level Local SGD for Heterogeneous Hierarchical Networkscited by 9
  16. 2019Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approachcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 11; 2025: 10; 2026: 3.

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  1. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  2. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  3. 2023Adaptive Federated Pruning in Hierarchical Wireless Networkscited by 10
  4. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  5. 2023Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaborationcited by 10
  6. 2023Delay-Aware Hierarchical Federated Learningcited by 9
  7. 2022MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networkscited by 5
  8. 2024Adaptive Federated Learning Over the Aircited by 4
  9. 2025Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edgecited by 4
  10. 2023Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learningcited by 3
  11. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  12. 2024Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimizationcited by 3
23 more
  1. 2024Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiencycited by 3
  2. 2023Data-Heterogeneous Hierarchical Federated Learning with Mobilitycited by 2
  3. 2024Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approachcited by 2
  4. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  5. 2024Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networkscited by 2
  6. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  7. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  8. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  9. 2023Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Taskscited by 1
  10. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  11. 2025Distribution-Aware Mobility-Assisted Decentralized Federated Learningcited by 1
  12. 2025Fast AI Model Partition for Split Learning over Edge Networkscited by 1
  13. 2026Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outlierscited by 1
  14. 2026C2FL: Clustered Continual Federated Learning under Spatial and Temporal Driftcited by 1
  15. 2023A Joint Gradient and Loss Based Clustered Federated Learning Designcited by 0
  16. 2024Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approachcited by 0
  17. 2024Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learningcited by 0
  18. 2024Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Networkcited by 0
  19. 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsificationcited by 0
  20. 2025Pinching Antennas Meet AI in Next-Generation Wireless Networkscited by 0
  21. 2025FedeCouple: Fine-Grained Balancing of Global-Generalization and Local-Adaptability in Federated Learningcited by 0
  22. 2025Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approachcited by 0
  23. 2026Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptationcited by 0