一个贝叶斯联合学习框架与在线拉普拉斯近似
相关实验视频
Updated: Jul 14, 2025

10:32
Image-based Lagrangian Particle Tracking in Bed-load Experiments
Published on: July 20, 2017
9.0K
IEEE transactions on pattern analysis and machine intelligence
|October 9, 2023
概括
本研究引入了一个新的联合学习 (FL) 框架,使用拉普拉斯近似来减少异质数据设置中的错误和遗忘. 这种新的方法在多个基准指标上取得了最先进的结果.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 允许在没有数据共享的情况下进行协作模式培训.
- 当前的FL方法经常平均参数,导致聚合错误和局部遗忘,特别是异质数据.
研究的目的:
- 提出一个新的联合学习框架,解决聚合错误和局部遗忘.
- 在异质数据环境中改进模型性能.
主要方法:
- 使用在线拉普拉斯近似用于客户端和服务器端的后方近似.
- 在服务器上采用多变量高斯积分机制来构建和最大化全局后置.
- 在客户端引入先前损失,以全球后置参数为指导,以减轻本地遗忘.
主要成果:
- 显著减少局部模型之间的差异引起的聚合错误.
- 通过对其他客户的学习约束来有效地减轻本地遗忘.
- 在多个基准数据集中实现最先进的性能.
结论:
- 拟议的联合学习框架与现有方法相比,表现优越.
- 在线拉普拉斯近似和一个新的客户端先前损失有效地解决异质FL设置中的挑战.
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