Kagoshima University · Graduate School of Science and Engineering · 教授

Research keywordsFault Tolerance・Neural Network・Parallel Computer・Qenetic Algorithin・センサネットワーク・ソフトコンピューティング・ニューラルネットワーク・フォールトトレランス

Research fieldsInformatics・Information network・情報科学・Sensitivity informatics/Soft computing・Integrated Science and Innovative Science・Integrated Disciplines・Comprehensive Fields・Complex systems

This is a public-data preview. Personalized fit, contact angles, and saved workflows require sign-in.

• Miyajima H., Shigei N., Miyajima H., Shiratori N. . Toward the development of learning methods with distributed processing using securely divided data . Computers and Electrical Engine2025 · 担当区分： 責任著者 記述言語： 英語 掲載種別： 研究論文（学術雑誌） 出版者・発行元： Computers and Electrical Engineering To pave the way to a super-smart society, artificial intelligence (AI) methods are being developed to discover and analyze necessary information instantly from cyberspace and utilize it in physical space. However, privacy protection is necessary for AI to process big data in cyberspace. From the viewpoint of developing safe and secure machine learning methods, research on (1) homomorphic cryptography, (2) differential privacy, (3) secure multiparty computation, and (4) federated learning is underway. The goal of these studies is to develop useful learning methods while maintaining data privacy. We propose a method to address the trade-off between security and usability in machine learning. This method balances usability and data confidentiality by using decomposed data to achieve secure distributed processing. However, such methods using distributed processing increase computational and communication overhead as the number of servers increases. To address this problem, we propose a method to control the computational complexity as the number of servers increases. On the basis of these studies, this study first systematically addresses the construction of secure distributed processing methods with decomposed data. A comprehensive approach is essential to advance the field and allow these methods to be effectively applied to different domains. On the basis of these methods, we propose back-propagation and neural gas learning methods with reduced computational and communication requirements. We then apply the proposed methods to numerical simulations of class classification and clustering problems and show that accuracy comparable to that of conventional models can be achieved with 1/Q computational and communication complexity for distributed models with Q servers. DOI： 10.1016/j.compeleceng.2025.110160 Scopus その他リンク： https://www.sciencedirect.com/science/article/pii/S004579062500103X
• Ryuo Kawabata, Kotaro Nagata, Yukari Nakamaru, Kentaro Yasui, Chihiro Morita, and Noritaka Shigei2025 · 担当区分： 最終著者, 責任著者 記述言語： 英語 掲載種別： 研究論文（国際会議プロシーディングス）
• 李 仕嘉, 重井 徳貴 . 室内人数推定のための低コスト MOX 式 CO2 センサ較正手法の一検討 . 日本知能情報ファジィ学会 ファジィ システム シンポジウム 講演論文集 44 - 47 2025年9月 詳細を見る 担当区分： 最終著2025 · 担当区分： 最終著者 記述言語： 日本語 掲載種別： 研究論文（研究会，シンポジウム資料等）
• Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori2025 · 記述言語： 英語 掲載種別： 研究論文（国際会議プロシーディングス）

• 超スマート社会におけるプライバシーを保護する機械学習に関する研究
• プロセッサアレイのフォールトトレランスに関する研究
• センサネットワークの稼働時間改善および低コスト実装に関する研究
• センサネットワークにおける通信制御と情報統合に関する研究

• Official profile
• KAKEN
• CiNii