科研成果详情

发表状态已发表Published
题名Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity
作者
发表日期2024-05-01
发表期刊IEEE Transactions on Artificial Intelligence
ISSN/eISSN2691-4581
卷号5期号:5页码:2026-2039
摘要

Federated learning (FL) is a decentralized learning method used to train machine learning algorithms. In FL, a global model iteratively collects the parameters of local models without accessing their local data. However, a significant challenge in FL is handling the heterogeneity of local data distribution, which often results in a drifted global model that is difficult to converge. To address this issue, current methods employ different strategies, such as knowledge distillation, weighted model aggregation, and multitask learning. These approaches are referred to as asynchronous FL, as they align user models either locally or posthoc, where model drift has already occurred or has been underestimated. In this article, we propose an active and synchronous correlation approach to address the challenge of user heterogeneity in FL. Specifically, our approach aims to approximate FL as standard deep learning by actively and synchronously scheduling user learning pace in each round with a dynamic multiphase curriculum. A global curriculum is formed by an autoregressive autoencoder that integrates all user curricula on the server. This global curriculum is then divided into multiple phases and broadcast to users to measure and align the domain-agnostic learning pace. Empirical studies demonstrate that our approach outperforms existing asynchronous approaches in terms of generalization performance, even in the presence of severe user heterogeneity.

关键词Curriculum learning (CL) federated learning (FL) heterogeneity data synchronization
DOI10.1109/TAI.2023.3307664
URL查看来源
语种英语English
Scopus入藏号2-s2.0-85168753609
引用统计
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11788
专题理工科技学院
通讯作者Guo, Jianxiong
作者单位
1.BNU-HKBU United International College, Guangdong Key Lab of AI and Multi-Modal Data Processing, Department of Computer Science, Zhuhai, 519087, China
2.Beijing Normal University, Advanced Institute of Natural Sciences, Zhuhai, 519087, China
3.BNU-HKBU United International College, Guangdong Key Lab of AI and Multi-Modal Data Processing, Zhuhai, 519087, China
第一作者单位北师香港浸会大学
通讯作者单位北师香港浸会大学
推荐引用方式
GB/T 7714
Wang, Mingjie,Guo, Jianxiong,Jia, Weijia. Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity[J]. IEEE Transactions on Artificial Intelligence, 2024, 5(5): 2026-2039.
APA Wang, Mingjie, Guo, Jianxiong, & Jia, Weijia. (2024). Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity. IEEE Transactions on Artificial Intelligence, 5(5), 2026-2039.
MLA Wang, Mingjie,et al."Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity". IEEE Transactions on Artificial Intelligence 5.5(2024): 2026-2039.
条目包含的文件
条目无相关文件。
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[Wang, Mingjie]的文章
[Guo, Jianxiong]的文章
[Jia, Weijia]的文章
百度学术
百度学术中相似的文章
[Wang, Mingjie]的文章
[Guo, Jianxiong]的文章
[Jia, Weijia]的文章
必应学术
必应学术中相似的文章
[Wang, Mingjie]的文章
[Guo, Jianxiong]的文章
[Jia, Weijia]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。