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FedeAMR-CFF: A Federated Automatic Modulation Recognition Method Based on Characteristic Feature Fine-Tuning.

Meng Zhang1, Jiankun Ma2, Zhenxi Zhang2

  • 1Southwest China Institute of Electronic Technology, Chengdu 610036, China.

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|July 12, 2025
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Summary
This summary is machine-generated.

Federated automatic modulation recognition (FedeAMR-CFF) enhances privacy by fine-tuning features, overcoming data silos and improving model performance in wireless communications.

Keywords:
automatic modulation recognitionfederated learningfine-tuning

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Deep learning-based automatic modulation recognition (DL-AMR) is crucial for intelligent systems.
  • Centralized DL-AMR risks privacy and communication overhead.
  • Decentralized training suffers from data disparities and insufficient samples.

Purpose of the Study:

  • To propose a privacy-preserving federated automatic modulation recognition method.
  • To address challenges of data privacy, communication overhead, and data silos.
  • To improve model performance in distributed wireless communication environments.

Main Methods:

  • Developed a federated automatic modulation recognition method based on characteristic feature fine-tuning (FedeAMR-CFF).
  • Clients extract features using distance-based metric screening.
  • Server aggregates model parameters using FedAvg and fine-tunes with collected features.

Main Results:

  • FedeAMR-CFF effectively safeguards client data privacy.
  • The method facilitates knowledge transfer across distributed datasets.
  • Experimental results show a 3.43% performance improvement over the best local model.

Conclusions:

  • FedeAMR-CFF offers a robust solution for privacy-preserving automatic modulation recognition.
  • The approach mitigates the non-independent and identically distributed (non-IID) problem.
  • This method enhances the applicability of DL-AMR in real-world wireless systems.