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Exploring personalized federated learning from a distribution-based perspective

delete2026-04-18
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OA
AI
T
Tianhao Yu
K
Kheng Cher Yeo
S
Sami Azam
X
Xiaohan Yu
H
Hui Chen
X
Xianxun Zhu *
DOI：10.1016/j.patcog.2026.113774delete
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摘要

摘要

En 中文
• 介绍了一种基于分布的贝叶斯PFL框架，超越了高斯假设。 • 采用秩-1参数化来降低计算和内存开销。 • 模型组特异性后验族以更好地捕捉异构客户端模式。 • 提供理论证明表明秩-1推断保留了局部方差结构。 • 在多个联邦基准测试中实现了更优的准确性和校准。
Keyword:
Federated learning
Bayesian inference
Uncertainty quantification
AI总结

AI总结

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期刊

Pattern Recognition 封面图
Pattern Recognition
IF:
7.6
论文数:
1.3W
被引数:
4.5W

机构

M
macquarie university
学者数:
2.2K
论文数: 1.1K
被引数: 0
C
charles darwin university
学者数:
171
论文数: 99
被引数: 0
S
shanghai university
学者数:
3.9W
论文数: 2.7W
被引数: 52
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引用论文

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