Abstract
Service robots and other smart devices, such as smartphones, have access to large amounts of data suitable for learning models, which can greatly improve the customer experience. Federated learning is a popular framework that allows multiple distributed devices to train deep learning models remotely, collaboratively, and preserving data privacy. However, little research has been done regarding the scenario where data distribution is non-identical among the participants and it also changes over time in unforeseen ways, causing what is known as concept drift. This situation is, however, very common in real life, and poses new challenges to both federated and continual learning. In this work, we propose an extension of the most widely known federated algorithm, FedAvg, adapting it for continual learning under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that our extended method outperforms the original one in this type of scenario.
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Acknowledgments
This research has received financial support from AEI/FEDER (EU) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria of Galicia (accreditation 2016–2019, ED431G/01 and ED431G/08, and reference competitive group ED431C2018/29), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154).
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Casado, F.E., Lema, D., Iglesias, R., Regueiro, C.V., Barro, S. (2021). Concept Drift Detection and Adaptation for Robotics and Mobile Devices in Federated and Continual Settings. In: Bergasa, L.M., Ocaña, M., Barea, R., López-Guillén, E., Revenga, P. (eds) Advances in Physical Agents II. WAF 2020. Advances in Intelligent Systems and Computing, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-62579-5_6
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DOI: https://doi.org/10.1007/978-3-030-62579-5_6
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