A Federated Unsupervised Personalisation for Cognitive Workload Estimation

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Accurate Cognitive Workload (CW) estimation, crucial in mobile healthcare and human-machine interaction, is impeded by client heterogeneity, data limitations, and privacy concerns, especially in the presence of Out-of-Distribution (OoD) clients. This study proposes a robust framework that is based on Federated Learning to protect data privacy, and utilizes context-based STRNet to enable joint cross-user learning on heterogeneous datasets, enhancing model generalisability. The framework includes a novel Unsupervised Client Personalisation strategy that prevents accuracy loss in OoD clients. We tested our framework on two publicly available CW datasets, COLET and ADABase. The framework improved the accuracy of centralized approaches while preserving data privacy. The framework is model-agnostic, efficient, and enables unsupervised personalisation for each client, bolstering the quality and robustness of the end-to-end deep learning models.




Fenoglio, D., Gjoreski, M., & Langheinrich, M. (2023). A Federated Unsupervised Personalisation for Cognitive Workload Estimation. In ACM International Conference Proceeding Series (pp. 520–522). Association for Computing Machinery. https://doi.org/10.1145/3626705.3631796

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