Federated Time Series Classification with ROCKET features

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.

  • Published in:
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
  • Type:
    Inproceedings
  • Authors:
    Casella, Bruno; Jakobs, Matthias; Aldinucci, Marco; Buschjäger, Sebastian
  • Year:
    2024

Citation information

Casella, Bruno; Jakobs, Matthias; Aldinucci, Marco; Buschjäger, Sebastian: Federated Time Series Classification with ROCKET features, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2024, October, Casella.etal.2024a,

Associated Lamarr Researchers

lamarr institute person Jakobs Matthias - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Matthias Jakobs

Scientist to the profile
lamarr institute person Buschjager Sebastian - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Dr. Sebastian Buschjäger

Scientific Coordinator Resource-aware ML to the profile