联邦AMR-CFF:基于特征特征微调的联邦自动调制识别方法.
Meng Zhang1, Jiankun Ma2, Zhenxi Zhang2
1Southwest China Institute of Electronic Technology, Chengdu 610036, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
联合自动调制识别 (FedeAMR-CFF) 通过微调功能来增强隐私,克服数据孤岛并改善无线通信中的模型性能.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 基于深度学习的自动调制识别 (DL-AMR) 对智能系统至关重要.
- 集中式DL-AMR风险隐私和通信开销.
- 分散培训受到了数据差异和样本不足的困扰.
研究的目的:
- 提出一种保护隐私的联合自动调制识别方法.
- 为了应对数据隐私,通信开销和数据孤岛的挑战.
- 在分布式无线通信环境中改进模型性能.
主要方法:
- 开发了一种基于特征特征微调 (FedeAMR-CFF) 的联合自动调制识别方法.
- 客户使用基于距离的度量选来提取特征.
- 服务器使用FedAvg汇总模型参数,并通过收集的特性进行微调.
主要成果:
- FedeAMR-CFF有效地保护客户数据的隐私.
- 该方法促进了分布式数据集的知识传输.
- 实验结果显示,与最佳本地模型相比,性能提高了3.43%.
结论:
- FedeAMR-CFF为保护隐私的自动调制识别提供了一个强大的解决方案.
- 这种方法减轻了非独立且分布相同 (非IID) 的问题.
- 这种方法提高了DL-AMR在现实世界无线系统中的适用性.
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