{"links":{},"id":232510,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232510","sets":["1164:5159:11541:11549"]},"path":["11549"],"owner":"44499","recid":"232510","title":["連合学習における更新情報の類似度に基づくビザンチン攻撃検出法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-02-22"},"_buckets":{"deposit":"23194898-dec5-447a-9eb9-54d7394b9771"},"_deposit":{"id":"232510","pid":{"type":"depid","value":"232510","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"連合学習における更新情報の類似度に基づくビザンチン攻撃検出法","author_link":["629469","629468","629467","629470"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"連合学習における更新情報の類似度に基づくビザンチン攻撃検出法"},{"subitem_title":"Byzantine attack detection via similarity of local updates in federated learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ポスターセッション1 SIP/EA","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-02-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"法政大学大学院理工学研究科"},{"subitem_text_value":"法政大学大学院理工学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Engineering, Hosei University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, Hosei University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/232510/files/IPSJ-SLP24151040.pdf","label":"IPSJ-SLP24151040.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP24151040.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"68ca538b-9a51-4a59-8df6-7855ee9f8639","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大野, 賢太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山岸, 昌夫"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kenta, Ohno","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masao, Yamagishi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10442647","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8663","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"小文では，連合学習におけるビザンチン攻撃を検出する手法，および，ビザンチン攻撃を繰り返すクライアントを検出する手法を提案している．ビザンチン攻撃は，悪意を持ったクライアントが中央サーバに対して偽の更新情報を伝達し，中央サーバでの学習を阻害する攻撃である．まず，更新情報のコサイン類似度の和を活用し，クライアントが送信してきた更新情報の真偽を他クライアントの更新情報との比較により判定する手法を提案している．次に，この手法を活用し，偽の更新情報を繰り返し送りつけてくるクライアントを特定する手法を提案している．さらに，各クライアントが多様なデータセットを保持している状況下において，提案手法を適用するためにはクライアントのグループ化が重要であることを明らかにしている．最後に，数値実験により，提案手法の有効性を確認している．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We propose a method to detect Byzantine attacks in federated learning, as well as a method for identifying clients repeating Byzantine attacks. The Byzantine attack is an attack that a malicious client sending false update information to the central server, in order to degradate learning performance at the central server. Firstly, we propose a method that utilizes a sum of cosine similarities of local update information to evaluate the authenticity of the local update sent by a client, through comparison with local updates from other clients. Next, by using this method, we also propose a method to identify clients repeatedly sending false update information. Furthermore, we clarify the importance of client grouping for enhance applicability of the proposed methods in scenarios where each client has diverse datasets. Finally, numerical experiments demonstrate the effectiveness of the proposed methods.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-02-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"40","bibliographicVolumeNumber":"2024-SLP-151"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:33:25.234388+00:00","updated":"2025-01-19T10:25:25.101169+00:00"}