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
644-649
页数6
ISBN(电子版)9781643684802
DOI
出版状态已出版 - 12 1月 2024
已对外发布
活动13th International Conference on Electronics, Communications and Networks, CECNet 2023 - Hybrid, Macau, 中国
期限: 17 11月 202320 11月 2023

出版系列

姓名Frontiers in Artificial Intelligence and Applications
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
Hybrid, Macau
时期17/11/2320/11/23

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