Data di Pubblicazione:
2024
Abstract:
This paper proposes FROCKS, a federated time series classification method using ROCKET features. Our approach dynamically adapts the models’ features by selecting and exchanging the best-performing ROCKET kernels from a federation of clients. Specifically, the server gathers the best-performing kernels of the clients together with the associated model parameters, and it performs a weighted average if a
kernel is best-performing for more than one client. We compare the proposed method with state-of-the-art approaches on the UCR archive binary classification datasets and show superior performance on most datasets.
Tipologia CRIS:
04A-Conference paper in volume
Elenco autori:
Casella, Bruno; Jakobs, Matthias; Aldinucci, Marco; Buschjäger, Sebastian
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Link al Full Text:
Titolo del libro:
ESANN 2024 Proceedings - 32th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning