Meta-learning and Personalization Layer in Federated Learning

被引:1
|
作者
Bao-Long Nguyen [1 ,2 ]
Tat Cuong Cao [1 ,2 ]
Bac Le [1 ,2 ]
机构
[1] Univ Sci, Fac Informat Technol, Ho Chi Minh City, Vietnam
[2] Vietnam Natl Univ, Ho Chi Minh City, Vietnam
关键词
Federated learning; Non-IID; Meta-learning; Personalization layer;
D O I
10.1007/978-3-031-21743-2_17
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Federated learning systems are confronted with two challenges: systemic and statistical. Non-IID data is acknowledged to be a primary component in causing statistical challenges. To address the federated learning system's substantial performance loss on non-IID data, we offer the FedMeta-Per algorithm (which combines meta-learning methods and personalization layer approaches into a federated learning system). In terms of performance and personalization, FedMeta-Per has been shown in experiments to outperform typical federated learning algorithm, algorithms using personalization layer techniques and algorithms using meta-learning in system optimization.
引用
收藏
页码:209 / 221
页数:13
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