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Distributed Collaborative Data Processing Framework for Unmanned Platforms Based on Federated Edge Intelligence.

Siyang Liu1, Nanliang Shan1,2, Xianqiang Bao1,2

  • 1National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China.

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|August 14, 2025
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Summary
This summary is machine-generated.

This study introduces a federated edge intelligence method (DSIA-FEI) to address data, device, and model heterogeneity in unmanned platforms. The novel approach improves collaborative processing accuracy and reduces communication rounds for autonomous systems.

Keywords:
data sharingedge computingfederated learninghierarchical parameter alignmentloss gradientsimilarity coefficientunmanned platforms

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Distributed Systems
  • Machine Learning

Background:

  • Unmanned platforms (UAVs, UGVs, AUVs) face significant challenges in collaborative data processing due to heterogeneous data, devices, and models.
  • Existing research inadequately addresses the combined issues of data, device, and model heterogeneity in unmanned systems.
  • The cloud-edge-end architecture offers a framework for distributed processing but requires specialized methods for effective collaboration.

Purpose of the Study:

  • To design a novel unmanned platform cluster architecture addressing data, device, and model heterogeneity.
  • To propose a federated edge intelligence method (DSIA-FEI) that integrates federated learning, edge computing, and distributed model training.
  • To mitigate the impact of data distribution heterogeneity and class imbalance in collaborative unmanned platform tasks.

Main Methods:

  • Designed an unmanned platform cluster architecture inspired by the cloud-edge-end model.
  • Developed a federated edge intelligence method (DSIA-FEI) with a data sharing mechanism and an intelligent model aggregation strategy.
  • Implemented hierarchical parameter alignment for mapping heterogeneous model parameters and similarity/loss gradient-based model selection for aggregation.

Main Results:

  • The DSIA-FEI method achieved high accuracy (0.91, 0.91, 0.88, 0.87) on FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, surpassing baseline methods by over 10%.
  • Reduced communication rounds by more than 40% compared to existing mainstream methods.
  • Demonstrated significant improvements in collaborative learning for unmanned platform swarms.

Conclusions:

  • The proposed DSIA-FEI method effectively addresses data, device, and model heterogeneity in unmanned platform collaborative processing.
  • The novel architecture and aggregation strategy enhance learning efficiency and accuracy in distributed autonomous systems.
  • The findings offer a robust solution for improving the performance of swarms of unmanned platforms in complex tasks.