Bold but cautious: Unlocking the potential of personalized federated learning through cautiously aggressive collaboration

X Wu, X Liu, J Niu, G Zhu… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Personalized federated learning (PFL) reduces the impact of non-independent and
identically distributed (non-IID) data among clients by allowing each client to train a …
被引用次数：6 相关文章

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

X Wu, X Liu, J Niu, G Zhu, S Tang - 2023 IEEE/CVF International …, 2023 - computer.org
Personalized federated learning (PFL) reduces the impact of non-independent and
identically distributed (non-IID) data among clients by allowing each client to train a …

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

X Wu, X Liu, J Niu, G Zhu, S Tang - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
Personalized federated learning (PFL) reduces the impact of non-independent and
identically distributed (non-IID) data among clients by allowing each client to train a …

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

X Wu, X Liu, J Niu, G Zhu, S Tang - 2023 IEEE/CVF …, 2023 - ieeexplore.ieee.org
Personalized federated learning (PFL) reduces the impact of non-independent and
identically distributed (non-IID) data among clients by allowing each client to train a …

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration (Supplemental Material)

X Wu, X Liu, J Niu, G Zhu, S Tang - Learning, 2022 - openaccess.thecvf.com
As we describe in section 1, a key factor affecting client parameter collaboration is the data
distribution difference between clients (ie, Ψ in Eq.(1)). However, due to privacy constraints …

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

X Wu, X Liu, J Niu, G Zhu, S Tang - arXiv preprint arXiv:2309.11103, 2023 - arxiv.org
Personalized federated learning (PFL) reduces the impact of non-independent and
identically distributed (non-IID) data among clients by allowing each client to train a …