PRIVATELY CUSTOMIZING PREFINETUNING TO BETTER MATCH USER DATA IN FEDERATED LEARNING

被引：0
|
作者：
Hou, Charlie [1 ]
Zhan, Hongyuan [2 ]
Shrivastava, Akshat [2 ]
Wang, Sid [2 ]
Livshits, Sasha [2 ]
Fanti, Giulia [1 ]
Lazar, Daniel [2 ]
机构：
[1] Department of Electrical and Computer Engineering, Carnegie Mellon University, United States
[2] Meta AI, United States
来源：
arXiv | 2023年
关键词：
Compilation and indexing terms; Copyright 2025 Elsevier Inc;
D O I：
暂无
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学科分类号：
摘要：
Machine learning - Privacy-preserving techniques
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