Adapted Weighted Aggregation in Federated Learning

被引：0

作者：

Tang, Yitong ^{[1
]}

机构：

[1] Univ British Columbia, Trusted & Efficient TEA Lab, 2329 West Mall, Vancouver, BC, Canada

来源：

THIRTY-EIGTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 21 | 2024年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study introduces FedAW, a novel federated learning algorithm that uses a weighted aggregation mechanism sensitive to the quality of client datasets, leading to better model performance and faster convergence on diverse datasets, validated using Colored MNIST.

引用

页码：23763 / 23765

页数：3