低延迟协作预测维护:在杂的工业环境中无线联合学习
1Department of Electrical Engineering, Mathematics and Science, University of Gävle, 801 76 Gävle, Sweden.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
工业4.0使边缘学习能够进行预测性维护,但通信延迟是一个挑战. 这项研究引入了一种新的方法,用于在联合学习中进行强大的无线模拟聚合,提高效率和准确性,即使在杂的频道上也是如此.
科学领域:
- 工业物联网和边缘计算
- 人工智能和机器学习
- 无线通信无线通信
背景情况:
- 工业4.0推动了互联工业资产的发展,为边缘设备学习产生了大量数据.
- 边缘学习对于预测性维护 (PM) 等应用程序至关重要,但通信延迟构成了重大瓶.
- 联合学习 (FL) 为边缘设备上的分布式模型培训提供了一个保护隐私的框架.
研究的目的:
- 解决工业4.0应用边缘学习中的通信延迟问题.
- 提出和评估一个模拟聚合无线方法,用于通过无线道进行联合学习 (FL).
- 为了减轻FL空中通信和计算 (FLOACC) 中频道噪声引起的性能下降.
主要方法:
- 实现了无线模拟聚合,利用波形叠加来减少通信延迟.
- 集成了一个基于跟踪的新型随机近似方案,与联邦随机差异减小梯度 (FSVRG).
- 开发了一种方法,在不增加传输功率的情况下减轻FLOACC中的通道噪声影响.
主要成果:
- 在各种FL场景中证明了拟议的FLOACC方法的卓越通信效率和可扩展性.
- 验证了强大的性能,即使在有噪音的频道.
- 在预测准确度和降低空中FL的模拟聚合损失函数方面取得了显著的改进.
结论:
- 拟议的方法有效地减轻了模拟无线联合学习中的通道噪声,确保了强大的性能.
- 这种方法为时间敏感的工业应用 (如预测性维护) 提供了一种通信效率高且可扩展的解决方案.
- 这些发现突显了无线模拟聚合在工业4.0环境中推进边缘学习的潜力.
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