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Federated learning on wearable devices: demo abstract

Published: 16 November 2020 Publication History
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  • Abstract

    Wearable devices collect user information about their activities and provide insights to improve their daily lifestyles. Smart health applications have achieved great success by training Machine Learning (ML) models on a large quantity of user data from wearables. However, user privacy and scalability are becoming critical challenges for training ML models in a centralized way. Federated learning (FL) is a novel ML paradigm with the goal of training high quality models while distributing training data over a large number of devices. In this demo, we present FL4W, a FL system with wearable devices enabling training a human activity recognition classifier. We also perform preliminary analytics to investigate the model performance with increasing computation of clients.

    References

    [1]
    Billur Barshan and Murat Cihan Yüksek. 2013. Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units. The Computer Journal 57, 11 (2013), 1649--1667.
    [2]
    Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao. 2020. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. IEEE Intelligent Systems 35,4 (2020), 83--93.
    [3]
    H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics.

    Cited By

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    • (2022)CloudyFLProceedings of the 6th International Workshop on Embedded and Mobile Deep Learning10.1145/3539491.3539592(13-18)Online publication date: 1-Jul-2022
    • (2022)Sustainability of Healthcare Data Analysis IoT-Based Systems Using Deep Federated LearningIEEE Internet of Things Journal10.1109/JIOT.2021.31036359:10(7338-7346)Online publication date: 15-May-2022
    • (2022)Human Activity Recognition with Smart Watches Using Federated LearningIntelligent and Fuzzy Systems10.1007/978-3-031-09176-6_9(77-85)Online publication date: 2-Jul-2022
    • Show More Cited By

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    Published In

    cover image ACM Conferences
    SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
    November 2020
    852 pages
    ISBN:9781450375900
    DOI:10.1145/3384419
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 November 2020

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    Author Tags

    1. federated learning
    2. human activity recognition
    3. wearable devices

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    • Short-paper

    Funding Sources

    • Academy of Finland
    • CERNET Innovation Project

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    Overall Acceptance Rate 174 of 867 submissions, 20%

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    SenSys '24

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    Cited By

    View all
    • (2022)CloudyFLProceedings of the 6th International Workshop on Embedded and Mobile Deep Learning10.1145/3539491.3539592(13-18)Online publication date: 1-Jul-2022
    • (2022)Sustainability of Healthcare Data Analysis IoT-Based Systems Using Deep Federated LearningIEEE Internet of Things Journal10.1109/JIOT.2021.31036359:10(7338-7346)Online publication date: 15-May-2022
    • (2022)Human Activity Recognition with Smart Watches Using Federated LearningIntelligent and Fuzzy Systems10.1007/978-3-031-09176-6_9(77-85)Online publication date: 2-Jul-2022
    • (2022)Security and Privacy Concerns for Healthcare Wearable Devices and Emerging Alternative ApproachesWireless Mobile Communication and Healthcare10.1007/978-3-031-06368-8_2(19-38)Online publication date: 7-Jun-2022

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