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Title: Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing
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.  more » « less
Award ID(s):
2144147
NSF-PAR ID:
10404518
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Technometrics
ISSN:
0040-1706
Page Range / eLocation ID:
1 to 12
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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