Federated Multi-Output Gaussian Processes
Federated Multi-Output Gaussian Processes
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DOI：
10.1080/00401706.2023.2238834
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发表时间：
2023-07
期刊：
影响因子：
2.5
通讯作者：
Seokhyun Chung;R. Kontar
中科院分区：
文献类型：
--
作者：
Seokhyun Chung;R. Kontar
摘要多输出高斯过程（MGP）回归在不同但相互关联的系统/单元的综合分析中起着重要作用。现有的MGP方法假设来自所有单元的数据被收集并存储在中央位置。这需要在中央位置处的大量计算和存储能力，由于原始数据交换而引起显著的通信流量，并且包括单元的隐私。然而，物联网技术的最新进展极大地提高了边缘计算能力，为解决这些挑战带来了重大机遇。在本文中，我们提出了FedMGP，这是一个通用的联邦分析（FA）框架，可以以分散的方式学习MGP，使用边缘计算能力来分配模型学习工作。具体来说，我们提出了一个层次化的建模方法，MGP是建立在共享的全球潜在功能。然后，我们开发了一个变分推理FA算法，克服了需要共享原始数据。相反，协作学习仅通过共享全局潜在函数统计来实现。全面的模拟研究和电池退化数据的案例研究突出了FedMGP的上级预测性能和多功能性，同时分布计算和存储需求，减少通信负担，促进隐私和个性化分析。
Abstract Multi-output Gaussian process (MGP) regression plays an important role in the integrative analysis of different but interrelated systems/units. Existing MGP approaches assume that data from all units is collected and stored at a central location. This requires massive computing and storage power at the central location, induces significant communication traffic due to raw data exchange, and comprises privacy of units. However, recent advances in Internet of Things technologies, which have tremendously increased edge computing power, pose a significant opportunity to address such challenges. In this article, we propose FedMGP, a general federated analytics (FA) framework to learn an MGP in a decentralized manner that uses edge computing power to distribute model learning efforts. Specifically, we propose a hierarchical modeling approach where an MGP is built upon shared global latent functions. We then develop a variational inference FA algorithm that overcomes the need to share raw data. Instead, collaborative learning is achieved by only sharing global latent function statistics. Comprehensive simulation studies and a case study on battery degradation data highlight the superior predictive performance and versatility of FedMGP, achieved while distributing computing and storage demands, reducing communication burden, fostering privacy, and personalizing analysis.