Deep Federated Feature Recommendation

Bin Xue; Qinghua Zheng; Zhinan Li; Weihu Zhao; Weihang Zhang; Xue Feng

doi:10.3233/FAIA231248

Deep Federated Feature Recommendation

Bin Xue^*, Qinghua Zheng, Zhinan Li, Weihu Zhao, Weihang Zhang, Xue Feng

^*此作品的通讯作者

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Feature recommendation is one of the most critical and challenging problems in modern digital intelligence system. However, it is difficult to ensure privacy protection in many situations. To address this challenge, a deep federated feature recommendation method, called DF_Rec, is designed. Particularly, deep decentralized federated aggregation learning (DFAL) is jointly developed based on the ingenious combination of several deep frameworks and federated aggregation schemes. Extensive experiments are performed on three authoritative datasets, demonstrating that DF_Rec outperforms existing outstanding systems significantly.

源语言	英语
主期刊名	Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023
编辑	Antonio J. Tallon-Ballesteros, Estefania Cortes-Ancos, Diego A. Lopez-Garcia
出版商	IOS Press BV
页	644-649
页数	6
ISBN（电子版）	9781643684802
DOI	https://doi.org/10.3233/FAIA231248
出版状态	已出版 - 12 1月 2024
已对外发布	是
活动	13th International Conference on Electronics, Communications and Networks, CECNet 2023 - Hybrid, Macau, 中国期限: 17 11月 2023 → 20 11月 2023

出版系列

姓名	Frontiers in Artificial Intelligence and Applications
卷	381
ISSN（印刷版）	0922-6389
ISSN（电子版）	1879-8314

会议

会议	13th International Conference on Electronics, Communications and Networks, CECNet 2023
国家/地区	中国
市	Hybrid, Macau
时期	17/11/23 → 20/11/23

访问文件

10.3233/FAIA231248

其它文件与链接

链接到 Scopus 的出版物

引用此

Xue, B., Zheng, Q., Li, Z., Zhao, W., Zhang, W., & Feng, X. (2024). Deep Federated Feature Recommendation. 在 A. J. Tallon-Ballesteros, E. Cortes-Ancos, & D. A. Lopez-Garcia (编辑), Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023 (页码 644-649). (Frontiers in Artificial Intelligence and Applications; 卷 381). IOS Press BV. https://doi.org/10.3233/FAIA231248

Xue, Bin ; Zheng, Qinghua ; Li, Zhinan 等. / Deep Federated Feature Recommendation. Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023. 编辑 / Antonio J. Tallon-Ballesteros ; Estefania Cortes-Ancos ; Diego A. Lopez-Garcia. IOS Press BV, 2024. 页码 644-649 (Frontiers in Artificial Intelligence and Applications).

@inproceedings{2d891faf918d4009afa54fbf0a8bb2e7,

title = "Deep Federated Feature Recommendation",

abstract = "Feature recommendation is one of the most critical and challenging problems in modern digital intelligence system. However, it is difficult to ensure privacy protection in many situations. To address this challenge, a deep federated feature recommendation method, called DF_Rec, is designed. Particularly, deep decentralized federated aggregation learning (DFAL) is jointly developed based on the ingenious combination of several deep frameworks and federated aggregation schemes. Extensive experiments are performed on three authoritative datasets, demonstrating that DF_Rec outperforms existing outstanding systems significantly.",

keywords = "deep learning, feature extraction-recommendation, Federated learning",

author = "Bin Xue and Qinghua Zheng and Zhinan Li and Weihu Zhao and Weihang Zhang and Xue Feng",

note = "Publisher Copyright: {\textcopyright} 2024 The authors and IOS Press.; 13th International Conference on Electronics, Communications and Networks, CECNet 2023 ; Conference date: 17-11-2023 Through 20-11-2023",

year = "2024",

month = jan,

day = "12",

doi = "10.3233/FAIA231248",

language = "English",

series = "Frontiers in Artificial Intelligence and Applications",

publisher = "IOS Press BV",

pages = "644--649",

editor = "Tallon-Ballesteros, {Antonio J.} and Estefania Cortes-Ancos and Lopez-Garcia, {Diego A.}",

booktitle = "Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023",

address = "Netherlands",

}

Xue, B, Zheng, Q, Li, Z, Zhao, W, Zhang, W & Feng, X 2024, Deep Federated Feature Recommendation. 在 AJ Tallon-Ballesteros, E Cortes-Ancos & DA Lopez-Garcia (编辑), Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023. Frontiers in Artificial Intelligence and Applications, 卷 381, IOS Press BV, 页码 644-649, 13th International Conference on Electronics, Communications and Networks, CECNet 2023, Hybrid, Macau, 中国, 17/11/23. https://doi.org/10.3233/FAIA231248

Deep Federated Feature Recommendation. / Xue, Bin; Zheng, Qinghua; Li, Zhinan 等.
Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023. 编辑 / Antonio J. Tallon-Ballesteros; Estefania Cortes-Ancos; Diego A. Lopez-Garcia. IOS Press BV, 2024. 页码 644-649 (Frontiers in Artificial Intelligence and Applications; 卷 381).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Deep Federated Feature Recommendation

AU - Xue, Bin

AU - Zheng, Qinghua

AU - Li, Zhinan

AU - Zhao, Weihu

AU - Zhang, Weihang

AU - Feng, Xue

PY - 2024/1/12

Y1 - 2024/1/12

N2 - Feature recommendation is one of the most critical and challenging problems in modern digital intelligence system. However, it is difficult to ensure privacy protection in many situations. To address this challenge, a deep federated feature recommendation method, called DF_Rec, is designed. Particularly, deep decentralized federated aggregation learning (DFAL) is jointly developed based on the ingenious combination of several deep frameworks and federated aggregation schemes. Extensive experiments are performed on three authoritative datasets, demonstrating that DF_Rec outperforms existing outstanding systems significantly.

AB - Feature recommendation is one of the most critical and challenging problems in modern digital intelligence system. However, it is difficult to ensure privacy protection in many situations. To address this challenge, a deep federated feature recommendation method, called DF_Rec, is designed. Particularly, deep decentralized federated aggregation learning (DFAL) is jointly developed based on the ingenious combination of several deep frameworks and federated aggregation schemes. Extensive experiments are performed on three authoritative datasets, demonstrating that DF_Rec outperforms existing outstanding systems significantly.

KW - deep learning

KW - feature extraction-recommendation

KW - Federated learning

UR - http://www.scopus.com/inward/record.url?scp=85183076145&partnerID=8YFLogxK

U2 - 10.3233/FAIA231248

DO - 10.3233/FAIA231248

M3 - Conference contribution

AN - SCOPUS:85183076145

T3 - Frontiers in Artificial Intelligence and Applications

SP - 644

EP - 649

BT - Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023

A2 - Tallon-Ballesteros, Antonio J.

A2 - Cortes-Ancos, Estefania

A2 - Lopez-Garcia, Diego A.

PB - IOS Press BV

T2 - 13th International Conference on Electronics, Communications and Networks, CECNet 2023

Y2 - 17 November 2023 through 20 November 2023

ER -

Xue B, Zheng Q, Li Z, Zhao W, Zhang W, Feng X. Deep Federated Feature Recommendation. 在 Tallon-Ballesteros AJ, Cortes-Ancos E, Lopez-Garcia DA, 编辑, Electronics, Communications and Networks - Proceedings of the 13th International Conference, CECNet 2023. IOS Press BV. 2024. 页码 644-649. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/FAIA231248

Deep Federated Feature Recommendation

摘要

出版系列

会议

访问文件

其它文件与链接

指纹

引用此