Abstract
Federated Learning (FL) has recently emerged as a promising method that employs a distributed learning model structure to overcome data privacy and transmission issues paused by central machine learning models. In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with a centralized model (server). However, this distributed learning approach presents unique learning challenges as the data used at local clients can be non-IID (Independent and Identically Distributed) and statistically diverse which decrease learning accuracy in the central model. In this paper, we overcome this problem by proposing a novel personalized federated learning method based One-Class Support Vector Machine (FedP-OCSVM) to personalize the resulting support vectors at each client. Our experimental validation showed that our FedP-OCSVM precisely constructed generalized clients’ models and thus achieved higher accuracy compared to other state-of-the-art methods.
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Notes
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The two bridges are operational and the companies which monitor them requested to keep the bridge name and the collected data about its health confidential.
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Anaissi, A., Suleiman, B., Alyassine, W. (2022). A Personalized Federated Learning Algorithm for One-Class Support Vector Machine: An Application in Anomaly Detection. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_31
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