mlsanityData to 7 Oct 2026

Adaptive Federated Pruning in Hierarchical Wireless Networks

arXiv 2305.09042 ↗First posted 15 May 2023cs.LGcited by 10

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Abstract (arXiv)

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a device-edge-cloud aggregation hierarchy, can enjoy both the cloud server's access to more datasets and the edge servers' efficient communications with devices. However, the learning latency increases with the HFL network scale due to the increasing number of edge servers and devices with limited local computation capability and communication bandwidth. To address this issue, in this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We present the convergence analysis of an upper on the l2 norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation. By decoupling the optimization problem and using Karush Kuhn Tucker (KKT) conditions, closed-form solutions of pruning ratio and wireless resource allocation are derived. Simulation results show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

30 of 46 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  3. 2018SNIP: Single-shot Network Pruning based on Connection Sensitivitycited by 802
  4. 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
  5. 2016Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networkscited by 541
  6. 2019Importance Estimation for Neural Network Pruningcited by 531
  7. 2020Pruning neural networks without any data by iteratively conserving synaptic flowcited by 475
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  9. 2018Sparsified SGD with Memorycited by 373
  10. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  11. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  12. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
18 more
  1. 2020Dynamic Model Pruning with Feedbackcited by 125
  2. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  3. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  4. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  5. 2021When to Prune? A Policy towards Early Structural Pruningcited by 38
  6. 2020Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learningcited by 37
  7. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  8. 2021Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferencecited by 31
  9. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  10. 2020Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systemscited by 17
  11. 2020Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learningcited by 15
  12. 2020Hierarchical Federated Learning through LAN-WAN Orchestrationcited by 13
  13. 2022Knowledge-aided Federated Learning for Energy-limited Wireless Networkscited by 8
  14. 2021In-network Computation for Large-scale Federated Learning over Wireless Edge Networkscited by 7
  15. 2021Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networkscited by 3
  16. 2021Relating CNNs with brain: Challenges and findingscited by 3
  17. 2019A Distributed Hierarchical SGD Algorithm with Sparse Global Reductioncited by 2
  18. 2021Research on Resource Allocation for Efficient Federated Learningcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 4; 2026: 1.

10 papers.

  1. 2023Green Edge AI: A Contemporary Surveycited by 24
  2. 2024Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learningcited by 3
  3. 2023Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networkscited by 2
  4. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  5. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  6. 2025TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learningcited by 1
  7. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  8. 2024CELLM: An Efficient Communication in Large Language Models Training for Federated Learningcited by 0
  9. 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Controlcited by 0
  10. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0