联邦高斯过程:融合,自动个性化和多忠度建模
IEEE transactions on pattern analysis and machine intelligence
|January 17, 2024
概括
我们介绍了联邦高斯过程 (GP) 回归 (FGPR),这是个性化,保护隐私的数据建模的新框架. FGPR有效地学习共享的先验和本地数据特征,以增强跨设备的回归性能.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 数据科学数据科学数据科学
背景情况:
- 联合学习可以在不共享原始数据的情况下进行协作模型培训.
- 高斯过程 (GPs) 是回归任务的强大的非参数模型.
- 个性化建模需要从单个数据集中捕获独特特征.
研究的目的:
- 提出FGPR,一个联邦高斯过程回归框架.
- 通过共同的先验来加强联合学习中的个性化.
- 为FGPR的融合提供理论保证,并证明其实际实用性.
主要方法:
- 联邦高斯过程 (GP) 回归 (FGPR) 框架.
- 使用平均化策略进行模型聚合.
- 通过随机梯度下降进行本地计算.
- 跨设备共享先验的联合学习.
主要成果:
- 通过将共享的先前与本地数据相结合,FGPR实现了个性化的预测.
- 理论分析表明,FGPR汇聚到日志边际概率的临界点.
- 广泛的案例研究证明了FGPR在各种回归任务中的有效性.
- FGPR被证明是保护隐私的多忠度数据建模的有希望的方法.
结论:
- FGPR为联合回归提供了一个强大的,保护隐私的方法.
- 该框架有效地平衡了全球知识共享与本地数据个性化.
- FGPR在相关的环境中推进了对联合学习的理论理解.
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