Decentralized Time Series Classification with ROCKET Features
Journal:
arXiv
Published Date:
Apr 24, 2025
Abstract
Time series classification (TSC) is a critical task with applications in
various domains, including healthcare, finance, and industrial monitoring. Due
to privacy concerns and data regulations, Federated Learning has emerged as a
promising approach for learning from distributed time series data without
centralizing raw information. However, most FL solutions rely on a
client-server architecture, which introduces robustness and confidentiality
risks related to the distinguished role of the server, which is a single point
of failure and can observe knowledge extracted from clients. To address these
challenges, we propose DROCKS, a fully decentralized FL framework for TSC that
leverages ROCKET (RandOm Convolutional KErnel Transform) features. In DROCKS,
the global model is trained by sequentially traversing a structured path across
federation nodes, where each node refines the model and selects the most
effective local kernels before passing them to the successor. Extensive
experiments on the UCR archive demonstrate that DROCKS outperforms
state-of-the-art client-server FL approaches while being more resilient to node
failures and malicious attacks. Our code is available at
https://anonymous.4open.science/r/DROCKS-7FF3/README.md.
