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Related Experiment Video

Updated: Jun 9, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Contribution-Based Resource Allocation for Effective Federated Learning in UAV-Assisted Edge Networks.

Gang Xiong1, Jincheng Guo2

  • 1The 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610000, China.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
Summary

This study proposes a new method for allocating network resources in federated learning (FL) using UAVs. It improves global model accuracy and convergence speed by prioritizing high-contribution clients for bandwidth allocation.

Keywords:
federated learningresource allocationunmanned aerial vehicle

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Area of Science:

  • Wireless Communication Networks
  • Edge Computing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Federated learning (FL) enables collaborative model training without sharing raw data.
  • Integrating Unmanned Aerial Vehicles (UAVs) as edge computing nodes presents unique resource allocation challenges in wireless networks.
  • Fair and efficient bandwidth allocation is crucial for optimizing FL performance in multi-tier network architectures.

Purpose of the Study:

  • To investigate a novel network resource allocation method for federated learning in a cloud-edge-client architecture.
  • To address fair bandwidth resource allocation challenges among FL clients.
  • To enhance the convergence speed and accuracy of the global FL model.

Main Methods:

  • Utilized UAVs as edge computing nodes within a three-layer wireless network.
  • Proposed a Shapley value (SV)-based contribution calculation strategy for model aggregation weights.
  • Developed a client selection and wireless resource allocation method prioritizing model contribution, reducing frequency for low-contribution clients.

Main Results:

  • The proposed method significantly reduced system delay and total energy consumption by 15%-50%.
  • Improved global model accuracy by 0.3% (short-term) and 2% (long-term).
  • Demonstrated enhanced convergence speed through optimized bandwidth allocation to high-contribution clients.

Conclusions:

  • The Shapley value-based resource allocation method effectively addresses fairness and efficiency in UAV-enabled federated learning.
  • Prioritizing high-contribution clients leads to substantial improvements in FL model performance and system efficiency.
  • This approach offers a viable solution for optimizing resource management in complex wireless federated learning environments.