Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing
Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing
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DOI：
10.1080/00401706.2022.2157882
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发表时间：
2022-12
期刊：
影响因子：
2.5
通讯作者：
Naichen Shi;R. Kontar
中科院分区：
文献类型：
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作者：
Naichen Shi;R. Kontar
摘要多年来，物联网（IoT）设备变得越来越强大。这提出了一个独特的机会，利用本地计算资源来分布模型学习，并规避共享原始数据的需要。底层的分布式和隐私保护数据分析方法通常被称为联邦学习（FL）。FL中的一个关键挑战是本地数据集之间的异质性。在这篇文章中，我们提出了一个新的个性化FL模型，PFL-DA，采用域适应的哲学。PFL-DA同时解决了数据异质性的两个来源：协变量和跨本地设备的概念转移。我们表明，无论是理论上还是经验上，PFL-DA克服了最先进的FL方法的固有缺点，并且能够在设备之间借用力量，同时允许它们保留自己的个性化模型。作为一个案例研究，我们将PFL-DA应用于分布式桌面3D打印，在那里我们获得了更准确的打印速度预测，这可以帮助提高打印机的效率。
Abstract Over the years, Internet of Things (IoT) devices have become more powerful. This sets forth a unique opportunity to exploit local computing resources to distribute model learning and circumvent the need to share raw data. The underlying distributed and privacy-preserving data analytics approach is often termed federated learning (FL). A key challenge in FL is the heterogeneity across local datasets. In this article, we propose a new personalized FL model, PFL-DA, by adopting the philosophy of domain adaptation. PFL-DA tackles two sources of data heterogeneity at the same time: a covariate and concept shift across local devices. We show, both theoretically and empirically, that PFL-DA overcomes intrinsic shortcomings in state of the art FL approaches and is able to borrow strength across devices while allowing them to retain their own personalized model. As a case study, we apply PFL-DA to distributed desktop 3D printing where we obtain more accurate predictions of printing speed, which can help improve the efficiency of the printers.