Privacy-protective Distributed Machine Learning between Rich Devices and Edge Servers

• Item type: Trans(1)
• 公開日: 2024-10-31
• タイトル: Privacy-protective Distributed Machine Learning between Rich Devices and Edge Servers
• タイトル / 言語: en
• タイトル: Privacy-protective Distributed Machine Learning between Rich Devices and Edge Servers
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [コンシューマ・サービス論文] edge computing, distributed machine learning, federated learning, internet of things
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Ochanomizu University
• 著者所属: University of Tokyo
• 著者所属: Kogakuin University
• 著者所属: Ochanomizu University
• 著者所属(英): en
• 著者所属(英): Ochanomizu University
• 著者所属(英): en
• 著者所属(英): University of Tokyo
• 著者所属(英): en
• 著者所属(英): Kogakuin University
• 著者所属(英): en
• 著者所属(英): Ochanomizu University
• 著者名: Saki, Takano; Akihiro, Nakao; Saneyasu, Yamaguch; Masato, Oguchi
• 著者名(英): Saki, Takano; Akihiro, Nakao; Saneyasu, Yamaguch; Masato, Oguchi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The utilization of data collected by edge devices, including personal information, in machine learning has emerged as a significant trend in recent years. In most distributed machine learning methods, like Federated Learning, data or training results are typically aggregated and managed on high-performance servers. However, transferring users' personal information to an external server can raise privacy concerns due to the potential risk of data leakage. To tackle this issue, we propose a distributed machine learning model that offers robust privacy protection, allowing users to decide whether or not to share personal data with the server. In the proposed model, the edge device takes over the training at the edge server and sends only the results for which the user has given permission to the edge server for integration. To validate the effectiveness of the proposed model, we performed experiments on facial image recognition using Jetson Nano as an edge device. The experimental results confirmed that edge devices were capable of utilizing personal information in a short time, while the edge server achieved enhanced accuracy by integrating multiple training results. Thus, the results show that the proposed model enables the safe and efficient utilization of data collected by edge devices.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The utilization of data collected by edge devices, including personal information, in machine learning has emerged as a significant trend in recent years. In most distributed machine learning methods, like Federated Learning, data or training results are typically aggregated and managed on high-performance servers. However, transferring users' personal information to an external server can raise privacy concerns due to the potential risk of data leakage. To tackle this issue, we propose a distributed machine learning model that offers robust privacy protection, allowing users to decide whether or not to share personal data with the server. In the proposed model, the edge device takes over the training at the edge server and sends only the results for which the user has given permission to the edge server for integration. To validate the effectiveness of the proposed model, we performed experiments on facial image recognition using Jetson Nano as an edge device. The experimental results confirmed that edge devices were capable of utilizing personal information in a short time, while the edge server achieved enhanced accuracy by integrating multiple training results. Thus, the results show that the proposed model enables the safe and efficient utilization of data collected by edge devices.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12628043
• 書誌情報: 情報処理学会論文誌コンシューマ・デバイス＆システム（CDS）; 巻 14, 号 3, 発行日 2024-10-31
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2186-5728
• 出版者 / 言語: ja
• 出版者: 情報処理学会