{"created":"2025-01-19T01:38:20.707582+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236345","sets":["6504:11678:11688"]},"path":["11688"],"owner":"44499","recid":"236345","title":["ブロックチェーン技術を活用したパーソナライズド連合学習におけるクラスタリングの基礎検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"7000eafa-c9f5-4410-be30-979d7e29668b"},"_deposit":{"id":"236345","pid":{"type":"depid","value":"236345","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ブロックチェーン技術を活用したパーソナライズド連合学習におけるクラスタリングの基礎検討","author_link":["646011","646009","646010"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ブロックチェーン技術を活用したパーソナライズド連合学習におけるクラスタリングの基礎検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2024-03-01","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"鹿児島大"},{"subitem_text_value":"鹿児島大"},{"subitem_text_value":"鹿児島大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/236345/files/IPSJ-Z86-1X-04.pdf","label":"IPSJ-Z86-1X-04.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-04"}],"format":"application/pdf","filename":"IPSJ-Z86-1X-04.pdf","filesize":[{"value":"322.6 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"8f962c2d-b6c3-42e1-81ec-7ba724d3bc65","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"内山, 光彩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鈴木, 昇太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小野, 智司"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"パーソナライズド連合学習は，個々の参加者が分布の異なるデータを持ち，所持するデータに適したモデルの構築を目的とする．しかし，所持するデータの不均一性によってモデルの性能は低下してしまう．この問題に対処するために，各参加者のモデルの推論類似度を用いてクラスタを形成することで対処するFederated Learning by Inference Similarity（FLIS）が提案されている．しかし，FLISを含む中央集約型の連合学習は，データと学習の過程を管理する中央サーバが脆弱性になり得る．このため本研究は，FLISの学習をブロックチェーンネットワーク上で実行する手法を提案する．提案手法は，FLISの性能を維持しつつ，中央サーバを用いることなく，パーソナライズド連合学習を可能とする．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"56","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"55","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":236345,"updated":"2025-01-19T09:17:49.573874+00:00","links":{}}