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of 813
pro vyhledávání: '"YANG Kan"'
The performance of clients in Federated Learning (FL) can vary due to various reasons. Assessing the contributions of each client is crucial for client selection and compensation. It is challenging because clients often have non-independent and ident
Externí odkaz:
http://arxiv.org/abs/2402.04409
Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem d
Externí odkaz:
http://arxiv.org/abs/2312.14941
Autor:
Ebron Jr., Sheldon C., Yang, Kan
Federated Learning (FL) enables collaborative machine learning model training across multiple parties without sharing raw data. However, FL's distributed nature allows malicious clients to impact model training through Byzantine or backdoor attacks,
Externí odkaz:
http://arxiv.org/abs/2311.10248
Publikováno v:
In European Journal of Medicinal Chemistry 5 September 2024 275
Publikováno v:
In Computer Networks October 2024 252
Publikováno v:
In Journal of Hydrology: Regional Studies June 2024 53
Autor:
Fan, Zeshuai, Hao, Yuchen, Huo, Yidan, Cao, Fei, Li, Longfei, Xu, Jianmei, Song, Yali, Yang, Kan
Publikováno v:
In European Journal of Medicinal Chemistry 5 May 2024 271
Autor:
Wang, Jincheng, Yu, Juehua, Wang, Mengdi, Zhang, Lingli, Yang, Kan, Du, Xiujuan, Wu, Jinyu, Wang, Xiaoqun, Li, Fei, Qiu, Zilong
Publikováno v:
In Biological Psychiatry 15 November 2023 94(10):792-803
Autor:
Wang, ShaSha, Huo, Yidan, Zhang, Jinmiao, Li, Longfei, Cao, Fei, Song, Yali, Zhang, Yajing, Yang, Kan
Publikováno v:
In Bioorganic & Medicinal Chemistry 1 October 2023 93
Akademický článek
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