Concept drift detection and adaptation for federated and continual learning

Estévez Casado, FernandoLema Pais, DylanFernández Criado, MarcosIglesias Rodríguez, RobertoVázquez Regueiro, CarlosBarro Ameneiro, Senén2021-07-292021-07-292021Casado, F.E., Lema, D., Criado, M.F. et al. Concept drift detection and adaptation for federated and continual learning. Multimed Tools Appl (2021). https://doi.org/10.1007/s11042-021-11219-x1380-7501http://hdl.handle.net/10347/26638Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known as concept drift. Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenarioeng© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Federated learningContinual learningNonstationarityConcept driftFederated AveragingCatastrophic forgettingRehearsalConcept drift detection and adaptation for federated and continual learningjournal article10.1007/s11042-021-11219-x1573-7721open access