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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring.

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Title: FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring.
Authors: Hu, Chun-Yu1,2,3 (AUTHOR), Hu, Li-Sha4 (AUTHOR), Yuan, Lin1,2 (AUTHOR), Lu, Dian-Jie3,5 (AUTHOR), Lyu, Lei3,5 (AUTHOR), Chen, Yi-Qiang6 (AUTHOR)
Source: Journal of Computer Science & Technology (10009000). Sep2023, Vol. 38 Issue 5, p970-984. 15p.
Subjects: Random forest algorithms, Federated learning, Forest health, Machine learning, Data protection, Data privacy
Abstract: Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption. However, most wearable health data is distributed across different organizations, such as hospitals, research institutes, and companies, and can only be accessed by the owners of the data in compliance with data privacy regulations. The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations. The second technical challenge is handling the dynamic expansion of the federation without model retraining. To address the first challenge, we propose a horizontal federated learning method called Federated Extremely Random Forest (FedERF). Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance. Based on FedERF, we present a federated incremental learning method called Federated Incremental Extremely Random Forest (FedIERF) to address the second technical challenge. FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally. The experiments show that FedERF achieves comparable performance with non-federated methods, and FedIERF effectively addresses the dynamic expansion of the federation. This opens up opportunities for cooperation between different organizations in wearable health monitoring. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 174014847
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PubType: Academic Journal
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  Data: FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computer+Science+%26+Technology+%2810009000%29%22">Journal of Computer Science & Technology (10009000)</searchLink>. Sep2023, Vol. 38 Issue 5, p970-984. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+health%22">Forest health</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+protection%22">Data protection</searchLink><br /><searchLink fieldCode="DE" term="%22Data+privacy%22">Data privacy</searchLink>
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  Data: Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption. However, most wearable health data is distributed across different organizations, such as hospitals, research institutes, and companies, and can only be accessed by the owners of the data in compliance with data privacy regulations. The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations. The second technical challenge is handling the dynamic expansion of the federation without model retraining. To address the first challenge, we propose a horizontal federated learning method called Federated Extremely Random Forest (FedERF). Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance. Based on FedERF, we present a federated incremental learning method called Federated Incremental Extremely Random Forest (FedIERF) to address the second technical challenge. FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally. The experiments show that FedERF achieves comparable performance with non-federated methods, and FedIERF effectively addresses the dynamic expansion of the federation. This opens up opportunities for cooperation between different organizations in wearable health monitoring. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11390-023-3009-0
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        Text: English
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        PageCount: 15
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      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Federated learning
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      – SubjectFull: Forest health
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      – SubjectFull: Machine learning
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      – SubjectFull: Data protection
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      – TitleFull: FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring.
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              M: 09
              Text: Sep2023
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