在联合学习中使用客户端无偏的骨动作识别器.
概括
对于骨架视频的联合学习得到了CSAR的改进,这是一种保存运动动态并减少偏差的新方法. 这种方法提高了分散式系统中的动作识别准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 边缘设备产生大量的用户数据,引发了对集中处理的隐私问题.
- 联邦学习 (FL) 提供分散的培训,但与骨架视频分析扎,未能保持运动动态,并表现出客户端异质性偏差.
研究的目的:
- 引入CSAR (Client-Unbiased Skeletal Action Recognizer for Federated Learning),这是一个新的框架,旨在克服FL对基于骨架的动作识别的局限性.
- 为了保持关键的运动动态,并减轻分散的学习环境中固有的分类器偏差.
主要方法:
- 在客户端培训期间,CSAR使用模型校准损失来对齐表示,并最大限度地减少客户端和服务器之间的数据漂移.
- 服务器端处理涉及类平衡的时空特征的原型高斯取样,通过运动感知差分损失来细化以捕获动态特性.
- 知识匹配用于增强全球模型理解和稳定.
主要成果:
- 通过CSAR,可以重新训练全球无基因识别器,其准确性与中央数据训练的模型相美.
- 实验表明在自然和标签异质性条件下,在最先进的方法中表现优越.
- 拟议的方法有效地保留了运动动态,并减轻了联合骨架动作识别中的客户端偏差.
结论:
- 通过使用联邦学习,CSAR为保护隐私的骨动作识别提供了一个强大的解决方案.
- 该框架成功地解决了保护运动动态和减轻分类器偏差的关键挑战.
- 对于分散的人类行动分析,CSAR提供了显著的进步,实现了高精度和概括性.
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