无线联合学习:现状,面临的挑战和未来的方向
Bingnan Xiao1, Xichen Yu1, Wei Ni2
1Key Laboratory for Information Science of Electromagnetic Waves (MoE), Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China.
Fundamental research
|August 8, 2025
概括
无线联合学习 (OTA-FL) 通过有效的数据聚合来解决无线网络瓶问题. 本综述探讨了OTA-FL的进展,挑战和未来的研究方向,以提高性能和可信度.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 通过无线网络快速增长的人工智能应用程序,造成了数据聚合瓶,特别是在网络边缘.
- 现有的解决方案难以满足对无线环境中高效和低延迟数据处理的日益增长的需求.
研究的目的:
- 提供对空中联合学习 (OTA-FL) 进展的全面审查.
- 为了对现有的OTA-FL系统进行分类并总结贡献.
- 为OTA-FL.确定挑战和未来的研究方向.
主要方法:
- 对OTA-FL研究的系统文献综述.
- 基于系统配置的OTA-FL的分类:单天线,多天线和可重新配置的智能表面辅助系统.
- 对安全,隐私和可信度方面的分析.
主要成果:
- OTA-FL有效地利用多接入通道叠加,以实现高效的频谱共享和全球模型聚合.
- 对OTA-FL系统的分类显示了各种配置的进展.
- 确定了包括模型扭曲,不平衡的数据聚合和有限的可访问性/可验证性在内的关键挑战.
结论:
- 在无线网络中,OTA-FL为高效的边缘智能提供了一个有希望的解决方案.
- 未来的研究应该专注于提高系统性能,可靠性和可信度.
- 解决道色和数据异质性等挑战对于OTA-FL的广泛采用至关重要.
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