在边缘人工智能和联合系统中进行特征压缩的适应性信息约束映射
1Vinnytsia National Technical University, Vinnytsia, Ukraine. kovtun_v_v@vntu.edu.ua.
Scientific reports
|August 22, 2025
概括
本研究介绍了边缘人工智能和联合学习中高效特征压缩的调模型. 它可以显著减少数据负载,同时在资源有限的设备上保持高准确性和语义准确性.
科学领域:
- 人工智能
- 机器学习
- 数据压缩
背景情况:
- 分布式智能系统面临着有限资源的挑战,影响边缘人工智能和联合学习.
- 由于服务质量不稳定,带宽有限和数据异质性,减少通讯开销至关重要.
研究的目的:
- 为资源有限的分布式智能系统开发高效的特征压缩模型.
- 解决边缘人工智能和联合学习环境中减少通信开销的需求.
主要方法:
- 开发了一种新型的调压缩模型,结合了变量隐性映射,非负性受约束的投影和随机布尔转换.
- 提出了一种包含Kullback-Leibler分歧和语义相关性保护的通用压缩质量函数.
- 为受限制的计算环境设计了高效的投影梯度优化算法.
主要成果:
- 在HAR和PAMAP2数据集上减少了6倍的负荷.
- 保持了超过94%的分类准确度,具有很高的语义准确度.
- 在低功耗设备 (Jetson Nano,Raspberry Pi 4) 上证明了对噪声和损耗的稳定性.
结论:
- 与SOTA解决方案相比,拟议的模型提供了更高的压缩效率,适应性和稳定性.
- 这种方法对边缘人工智能和联合学习有效,特别是在不稳定的传输条件下.
- 在现实数据集和低功耗硬件上验证了实用的有效性.
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