Vote for your favorite mineral in #MinCup25! - Kosmochlor vs. Titanite
We're going for namesake-minerals as bright meteorite-derived green #kosmochlor is up against the wedge-shaped indicator mineral #titanite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Wang, Xiaoya, Zhu, Tianqing, Zhou, Wanlei (2024) Supplement data in federated learning with a generator transparent to clients. Information Sciences, 666. 120437 doi:10.1016/j.ins.2024.120437

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleSupplement data in federated learning with a generator transparent to clients
JournalInformation Sciences
AuthorsWang, XiaoyaAuthor
Zhu, TianqingAuthor
Zhou, WanleiAuthor
Year2024 (May)Volume666
PublisherElsevier BV
DOIdoi:10.1016/j.ins.2024.120437Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID17271490Long-form Identifiermindat:1:5:17271490:7
GUID0
Full ReferenceWang, Xiaoya, Zhu, Tianqing, Zhou, Wanlei (2024) Supplement data in federated learning with a generator transparent to clients. Information Sciences, 666. 120437 doi:10.1016/j.ins.2024.120437
Plain TextWang, Xiaoya, Zhu, Tianqing, Zhou, Wanlei (2024) Supplement data in federated learning with a generator transparent to clients. Information Sciences, 666. 120437 doi:10.1016/j.ins.2024.120437
In(2024) Information Sciences Vol. 666. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Zhang, Lefeng, Zhu, Tianqing, Xiong, Ping, Zhou, Wanlei, Yu, Philip S. (2023) A Robust Game-Theoretical Federated Learning Framework With Joint Differential Privacy. IEEE Transactions on Knowledge and Data Engineering, 35 (4) 3333-3346 doi:10.1109/tkde.2021.3140131
Zhang, Lefeng, Zhu, Tianqing, Xiong, Ping, Zhou, Wanlei, Yu, Philip S. (2023) A Game-Theoretic Federated Learning Framework for Data Quality Improvement. IEEE Transactions on Knowledge and Data Engineering, 35 (11) 10952-10966 doi:10.1109/tkde.2022.3230959
Li, Fangyu, Chen, Xuqiang, Han, Zhu, Du, Yongping, Han, Honggui (2024) Federated learning via reweighting information bottleneck with domain generalization. Information Sciences, 677. 120825 doi:10.1016/j.ins.2024.120825
Yang, He, Xi, Wei, Wang, Zizhao, Shen, Yuhao, Ji, Xinyuan, Sun, Cerui, Zhao, Jizhong (2023) FedRich: Towards efficient federated learning for heterogeneous clients using heuristic scheduling. Information Sciences, 645. 119360 doi:10.1016/j.ins.2023.119360
Chang, Wenhan, Zhu, Tianqing (2024) Gradient-based defense methods for data leakage in vertical federated learning. Computers & Security, 139. 103744 doi:10.1016/j.cose.2024.103744
Cao, Fan, Liu, Bo, He, Jinghui, Xu, Jian, Xiao, Yanshan (2024) Privacy preservation-based federated learning with uncertain data. Information Sciences, 678. 121024 doi:10.1016/j.ins.2024.121024
Zhao, Xingying, Shen, Dong (2024) FedSW: Federated learning with adaptive sample weights. Information Sciences, 654. 119873 doi:10.1016/j.ins.2023.119873
Zhou, Xingcai, Yang, Guang (2024) Communication-efficient and privacy-preserving large-scale federated learning counteracting heterogeneity. Information Sciences, 661. 120167 doi:10.1016/j.ins.2024.120167
Zhou, Yuhao, Shi, Minjia, Tian, Yuxin, Ye, Qing, Lv, Jiancheng (2024) DeFTA: A plug-and-play peer-to-peer decentralized federated learning framework. Information Sciences, 670. 120582 doi:10.1016/j.ins.2024.120582
Zhang, Tao, Zhu, Tianqing, Li, Jing, Han, Mengde, Zhou, Wanlei, Yu, Philip S. (2022) Fairness in Semi-Supervised Learning: Unlabeled Data Help to Reduce Discrimination. IEEE Transactions on Knowledge and Data Engineering, 34 (4) 1763-1774 doi:10.1109/tkde.2020.3002567
Wang, Jinbo, Pei, Xikai, Wang, Ruijin, Zhang, Fengli, Chen, Ting (2024) Federated semi-supervised learning with tolerant guidance and powerful classifier in edge scenarios. Information Sciences, 662. 120201 doi:10.1016/j.ins.2024.120201
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 21, 2025 06:33:27
Go to top of page