Ensemble and continual federated learning for classification tasks
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Ensemble and continual federated learning for classification tasksData
2023-09Cita bibliográfica
Casado, F.E., Lema, D., Iglesias, R. et al. Ensemble and continual federated learning for classification tasks. Mach Learn 112, 3413–3453 (2023). https://doi.org/10.1007/s10994-023-06330-z
Resumo
[Abstract]: Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have been successfully applied to real use cases. The vast majority of work in federated learning assumes static datasets and relies on the use of deep neural networks. However, in real-world problems, it is common to have a continual data stream, which may be non-stationary, leading to phenomena such as concept drift. Besides, there are many multi-device applications where other, non-deep strategies are more suitable, due to their simplicity, explainability, or generalizability, among other reasons. In this paper we present Ensemble and Continual Federated Learning, a federated architecture based on ensemble techniques for solving continual classification tasks. We propose the global federated model to be an ensemble, consisting of several independent learners, which are locally trained. Thus, we enable a flexible aggregation of heterogeneous client models, which may differ in size, structure, or even algorithmic family. This ensemble-based approach, together with drift detection and adaptation mechanisms, also allows for continual adaptation in situations where data distribution changes over time. In order to test our proposal and illustrate how it works, we have evaluated it in different tasks related to human activity recognition using smartphones.
Palabras chave
Concept drift
Ensemble learning
Federated learning
Semi-supervised classification
Smartphones
Ensemble learning
Federated learning
Semi-supervised classification
Smartphones
Descrición
Financiado para publicación en acceso aberto: CRUE-CSIC/Springer Nature. The raw data used in the experiments described in Sect. 7 and Appendix A can be found on the following URL: https://citius.usc.es/t/30.
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Dereitos
Attribution 4.0 International (CC BY)
ISSN
0885-6125