{"updated":"2025-01-19T19:45:07.962336+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00205414","sets":["6504:10247:10252"]},"path":["10252"],"owner":"6748","recid":"205414","title":["サーバレスFederated Learningのための分散最適化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-20"},"_buckets":{"deposit":"70fa12cd-5e85-42d3-bd6c-70371ed6277c"},"_deposit":{"id":"205414","pid":{"type":"depid","value":"205414","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"サーバレスFederated Learningのための分散最適化","author_link":["509671","509672","509668","509669","509670"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"サーバレスFederated Learningのための分散最適化"}]},"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":"2020-02-20","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":"青学大"},{"subitem_text_value":"青学大"},{"subitem_text_value":"京大"},{"subitem_text_value":"京大"},{"subitem_text_value":"京大"}]},"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/205414/files/IPSJ-Z82-2E-05.pdf","label":"IPSJ-Z82-2E-05.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-19"}],"format":"application/pdf","filename":"IPSJ-Z82-2E-05.pdf","filesize":[{"value":"791.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"eb302eb7-d784-4e0b-81ea-9286d93814ec","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"田谷, 昭仁"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"戸辺, 義人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西尾, 理志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"守倉, 正博"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 高至"}],"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":"FL（Federarted Learning）とはスマートフォン等のユーザ端末を使って分散機械学習を行い、学習後の機械学習モデルのみをサーバにアップロードし集約する仕組みであり、プライバシーに関わるデータがサーバにアップロードされることを回避するために提案された。しかし、既存のFLではユーザ数が増えたときにサーバへの通信負荷が大きくなることが予想される。本稿では、大規模化に向けて、端末間通信を活用することでサーバレスFLを実現する手法を提案する。提案手法は分散合意最適化を関数空間に適用することで実現しており、co-distillationとの類似性についても議論する。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"20","bibliographic_titles":[{"bibliographic_title":"第82回全国大会講演論文集"}],"bibliographicPageStart":"19","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-19T01:07:34.122364+00:00","id":205414,"links":{}}