Fang L. et al. (2021) Bayesian Inference Federated Learning for Heart Rate Prediction. In: Ye J., O’Grady M.J., Civitarese G., Yordanova K. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-70569-5_8
Bayesian inference federated learning for heart rate prediction
|Author:||Fang, Lei1; Liu, Xiaoli2; Su, Xiang2,3;|
1University of St Andrews, St Andrews, KY16 9SX, UK
2University of Helsinki, Helsinki, 00014, Finland
3University of Oulu, Oulu, 90014, Finland
4The Hong Kong University of Science and Technology, Hong Kong
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021102151851
|Publish Date:|| 2021-10-21
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.
Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
|Pages:||116 - 130|
Wireless Mobile Communication and Healthcare : 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings
|Host publication editor:||
O'Grady, M. J.
International Conference on Wireless Mobile Communication and Healthcare
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This 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.
|Academy of Finland Grant Number:||
326305 (Academy of Finland Funding decision)
319670 (Academy of Finland Funding decision)
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021. This is a post-peer-review, pre-copyedit version of an article published in Wireless Mobile Communication and Healthcare : 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-70569-5_8.