Federating Time Series Classification methods

Time series classification (TSC) is a pivotal task in modern machine learning, with widespread applications in fields such as healthcare, finance, and cybersecurity. However, TSC techniques are subject to many challenges, such as resource constraints and data privacy. Data privacy is a critical concern, particularly in key applications such as healthcare and finance. In these contexts, the use of sensitive or personal data is often governed by strict regulations such as the General Data Protection Regulation (GDPR). To address this challenge, Federated Learning (FL) presents a natural and effective solution for collaboratively training models across decentralized data sources while preserving user privacy. Most existing FL algorithms primarily rely on Neural Network (NN) models. However, recent advancements began to explore the use of traditional machine learning (ML) methods in FL settings. This thesis aims to adapt/provide a federated version of an already existing TSC algorithm.

The student is first supposed to familiarize with FL and to have a careful review of TSC literature. Then, the student should identify one or more TSC techniques of interest that could be federated. A set of experiments on univariate and/or multivariate datasets (like the UCR and the UEA archives) should be conducted. Finally, depending on the achieved results, it could be possible to work towards a conference/journal publication. Some FL and TSC papers are included in the bibliography section, as well as some recent papers proposing a federated version of already existing TSC methods.

Under the guidance of a team of researchers, the student will first be required to study recent works on the subject before being practically introduced to the task by experimenting with existing code.

• The candidate should have good programming skills (Python and PyTorch) and be motivated. A previous knowledge of the topic would be preferred but not required.

1. McMahan B., et al., 'Communication-Efficient Learning of Deep Networks from Decentralized Data', https://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
2. Dempster A., 'ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels', Data Mining and Knowledge Discovery, 2020
3. Middlehurst M., 'HIVE-COTE 2.0: a new meta ensemble for time series classification', Machine Learning, 2021
4. Schäfer P., 'The BOSS is concerned with time series classification in the presence of noise', Data Mining and Knowledge Discovery, 29(6): 2015
5. Casella B., 'Federated Time Series Classification with Rocket features', Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, 2024
6. Casella B., 'Fed2RC: Federated Rocket Kernels and Ridge Classifier for Time Series Classification', 28th European Conference on Artificial Intelligence, ECAI 2025.