mlsanityData to 7 Oct 2026

Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity

arXiv 2308.01562 ↗First posted 3 Aug 2023eess.SY cs.LG cs.NIcited by 10

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Abstract (arXiv)

While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth. Owing to these practical constraints and system models, this paper leverages model pruning and proposes a pruning-enabled hierarchical federated learning (PHFL) in heterogeneous networks (HetNets). We first derive an upper bound of the convergence rate that clearly demonstrates the impact of the model pruning and wireless communications between the clients and the associated BS. Then we jointly optimize the model pruning ratio, central processing unit (CPU) frequency and transmission power of the clients in order to minimize the controllable terms of the convergence bound under strict delay and energy constraints. However, since the original problem is not convex, we perform successive convex approximation (SCA) and jointly optimize the parameters for the relaxed convex problem. Through extensive simulation, we validate the effectiveness of our proposed PHFL algorithm in terms of test accuracy, wall clock time, energy consumption and bandwidth requirement.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 3 Aug 2023.

Builds on

24 of 37 references.

References · 24

  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
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  12. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
12 more
  1. 2020Dynamic Model Pruning with Feedbackcited by 125
  2. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  3. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  4. 2020Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networkscited by 42
  5. 2021Anarchic Federated Learningcited by 42
  6. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  7. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  8. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  9. 2018Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systemscited by 8
  10. 2023FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learningcited by 7
  11. 2020Communication Efficient Federated Learning with Energy Awareness over Wireless Networkscited by 6
  12. 2022Towards Communication-Learning Trade-off for Federated Learning at the Network Edgecited by 5

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Cited by year

Citing papers per year: 2023: 2; 2024: 6; 2025: 2.

10 papers.

  1. 2024Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networkscited by 4
  2. 2024Hierarchical Federated ADMMcited by 4
  3. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  4. 2023Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networkscited by 1
  5. 2024Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrivalcited by 1
  6. 2024Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budgetcited by 1
  7. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  8. 2024Personalized Hierarchical Split Federated Learning in Wireless Networkscited by 0
  9. 2025Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehiclescited by 0
  10. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0