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pro vyhledávání: '"Guo, Kaiyang"'
Federated Learning (FL) involves training a model over a dataset distributed among clients, with the constraint that each client's dataset is localized and possibly heterogeneous. In FL, small and noisy datasets are common, highlighting the need for
Externí odkaz:
http://arxiv.org/abs/2312.09817
Model-based offline reinforcement learning (RL) aims to find highly rewarding policy, by leveraging a previously collected static dataset and a dynamics model. While the dynamics model learned through reuse of the static dataset, its generalization a
Externí odkaz:
http://arxiv.org/abs/2210.06692
Autor:
Hasan, Mohsin, Zhang, Zehao, Guo, Kaiyang, Karami, Mahdi, Zhang, Guojun, Chen, Xi, Poupart, Pascal
Making predictions robust is an important challenge. A separate challenge in federated learning (FL) is to reduce the number of communication rounds, particularly since doing so reduces performance in heterogeneous data settings. To tackle both issue
Externí odkaz:
http://arxiv.org/abs/2206.09526
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a novel personalized federated learning method via Bayesian variational
Externí odkaz:
http://arxiv.org/abs/2206.07977
In typical scenarios where the Federated Learning (FL) framework applies, it is common for clients to have insufficient training data to produce an accurate model. Thus, models that provide not only point estimations, but also some notion of confiden
Externí odkaz:
http://arxiv.org/abs/2206.06357
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Akademický článek
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Publikováno v:
2022 11th International Conference on Communications, Circuits and Systems (ICCCAS).
Publikováno v:
2022 3rd Information Communication Technologies Conference (ICTC).
Publikováno v:
Security and Communication Networks.
Aiming at the problem of illegal data sharing of malicious users in the access control scheme based on attribute-based encryption, an access control scheme that can restrict the sending ability of data owners is proposed. By adding a sanitizer to san