Federated Time Series Classification with ROCKET features
Bruno Casella, Matthias Jakobs, Marco Aldinucci, Sebastian Buschjäger · 2024
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 bestperforming 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.