Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services
By training a multivariate deep learning model distributed across existing IoT services using vertical federated learning, expanded services could be constructed cost-effectively while preserving the independent data architecture of each service. Previously, we proposed a design approach for vertica...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11977 |
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| author | Soyeon Oh Minsoo Lee |
| author_facet | Soyeon Oh Minsoo Lee |
| author_sort | Soyeon Oh |
| collection | DOAJ |
| description | By training a multivariate deep learning model distributed across existing IoT services using vertical federated learning, expanded services could be constructed cost-effectively while preserving the independent data architecture of each service. Previously, we proposed a design approach for vertical federated learning considering IoT domain characteristics. Also, our previous method, designed leveraging our approach, achieved improved performance, especially in IoT domains, compared to other representative vertical federated learning mechanisms. However, our previous method was difficult to apply in real-world scenarios because its mechanism consisted of several options. In this paper, we propose a new vertical federated learning method, TT-VFDL-ST (Task-driven Transferred Vertical Federated Deep Learning using Self-Transfer partial training), a consistent single mechanism even in various real-world scenarios. The proposed method is also designed based on our previous design approach. However, the difference is that it leverages a newly proposed self-transfer partial training mechanism. The self-transfer partial training mechanism improved the MSE and accuracy of TT-VFDL-ST by 0.00262 and 12.08% on average compared to existing mechanisms. In addition, MSE and accuracy improved by up to 0.00290 and 5.08% compared to various options of our previous method. By applying the self-transfer partial training mechanism, TT-VFDL-ST could be used as a key solution to construct real-world-integrated IoT services. |
| format | Article |
| id | doaj-art-5b565624019c48d7a4da1bc5eb9cb67a |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5b565624019c48d7a4da1bc5eb9cb67a2025-08-20T02:55:31ZengMDPI AGApplied Sciences2076-34172024-12-0114241197710.3390/app142411977Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT ServicesSoyeon Oh0Minsoo Lee1Department of Computer Science and Engineering, Ewha Womans University, Seoul 03760, Republic of KoreaDepartment of Computer Science and Engineering, Ewha Womans University, Seoul 03760, Republic of KoreaBy training a multivariate deep learning model distributed across existing IoT services using vertical federated learning, expanded services could be constructed cost-effectively while preserving the independent data architecture of each service. Previously, we proposed a design approach for vertical federated learning considering IoT domain characteristics. Also, our previous method, designed leveraging our approach, achieved improved performance, especially in IoT domains, compared to other representative vertical federated learning mechanisms. However, our previous method was difficult to apply in real-world scenarios because its mechanism consisted of several options. In this paper, we propose a new vertical federated learning method, TT-VFDL-ST (Task-driven Transferred Vertical Federated Deep Learning using Self-Transfer partial training), a consistent single mechanism even in various real-world scenarios. The proposed method is also designed based on our previous design approach. However, the difference is that it leverages a newly proposed self-transfer partial training mechanism. The self-transfer partial training mechanism improved the MSE and accuracy of TT-VFDL-ST by 0.00262 and 12.08% on average compared to existing mechanisms. In addition, MSE and accuracy improved by up to 0.00290 and 5.08% compared to various options of our previous method. By applying the self-transfer partial training mechanism, TT-VFDL-ST could be used as a key solution to construct real-world-integrated IoT services.https://www.mdpi.com/2076-3417/14/24/11977vertical federated learningdeep learningtransfer learningIoTtime-series |
| spellingShingle | Soyeon Oh Minsoo Lee Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services Applied Sciences vertical federated learning deep learning transfer learning IoT time-series |
| title | Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services |
| title_full | Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services |
| title_fullStr | Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services |
| title_full_unstemmed | Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services |
| title_short | Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services |
| title_sort | consistent vertical federated deep learning using task driven features to construct integrated iot services |
| topic | vertical federated learning deep learning transfer learning IoT time-series |
| url | https://www.mdpi.com/2076-3417/14/24/11977 |
| work_keys_str_mv | AT soyeonoh consistentverticalfederateddeeplearningusingtaskdrivenfeaturestoconstructintegratediotservices AT minsoolee consistentverticalfederateddeeplearningusingtaskdrivenfeaturestoconstructintegratediotservices |