Bayesian inference federated learning for heart rate prediction
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The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users’ devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices.
Fang , L , Liu , X , Su , X , Ye , J , Dobson , S , Hui , P & Tarkoma , S 2021 , Bayesian inference federated learning for heart rate prediction . in J Ye , M J O'Grady , G Civitarese & K Yordanova (eds) , Wireless Mobile Communication and Healthcare : 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings . Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , vol. 362 LNICST , Springer , Cham , pp. 116-130 . https://doi.org/10.1007/978-3-030-70569-5_8
Wireless Mobile Communication and Healthcare
Copyright © 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-030-70569-5_8.
DescriptionThis work has been partially supported by the UK EPSRC under grant number EP/N007565/1, “Science of Sensor Systems Software”, and by Academy of Finland projects, grant number 325774, 3196669, 319670, 326305, and 325570.
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