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FedADT:一种基于衍生术语的自适应方法,用于联合学习
Huimin Gao1, Qingtao Wu1,2, Xuhui Zhao1
1School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
基于衍生术语 (FedADT) 的联邦自适应学习通过使用自适应步骤和梯度差异来改进联合学习. 这种新的方法增强了模型的融合,并减少了分布式培训中的噪音敏感性.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
- 计算机视觉 计算机视觉
背景情况:
- 联合学习可以在本地数据上进行协作模型培训.
- 挑战包括标准联合学习中的缓慢融合,缺乏适应性和噪音敏感性.
研究的目的:
- 引入基于衍生术语 (FedADT) 的联合适应性学习,以解决联合学习的局限性.
- 在分布式培训中增强融合速度和对噪声的稳定性.
主要方法:
- 在本地模型更新中,FedADT集成了适应性步骤大小和梯度差异.
- 移动平均线的衰减应用于衍生项,以减轻噪音.
- 对非凸的客观函数进行了收分析.
主要成果:
- 通过适当的超参数调整,FedADT可以达到1/nT的合率.
- 关于图像分类 (MNIST,时尚MNIST) 的实验证明了FedADT的有效性.
- 接收器运行特征曲线验证了服装类别预测中的性能.
结论:
- FedADT提供了一种有效的解决方案,用于提高联合学习绩效.
- 拟议的方法提高了分布式培训环境中的融合和噪声稳定性.
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