Federated Learning on Multimodal Data: A Comprehensive Survey

doi:10.1007/s11633-022-1398-0

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Federated Learning on Multimodal Data: A Comprehensive Survey
	Yi-Ming Lin, Yuan Gao, Mao-Guo Gong, Si-Jia Zhang, Yuan-Qiao Zhang, Zhi-Yuan Li
刊名	Machine Intelligence Research
	2023
卷号	20 期号:4 页码:539-553
关键词	Federated learning, multimodal learning, heterogeneous data, edge computing, collaborative learning
ISSN号	2731-538X
DOI	10.1007/s11633-022-1398-0
英文摘要	With the growing awareness of data privacy, federated learning (FL) has gained increasing attention in recent years as a major paradigm for training models with privacy protection in mind, which allows building models in a collaborative but private way without exchanging data. However, most FL clients are currently unimodal. With the rise of edge computing, various types of sensors and wearable devices generate a large amount of data from different modalities, which has inspired research efforts in multimodal federated learning (MMFL). In this survey, we explore the area of MMFL to address the fundamental challenges of FL on multimodal data. First, we analyse the key motivations for MMFL. Second, the currently proposed MMFL methods are technically classified according to the modality distributions and modality annotations in MMFL. Then, we discuss the datasets and application scenarios of MMFL. Finally, we highlight the limitations and challenges of MMFL and provide insights and methods for future research.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/52348]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	School of Electronic Engineering, Xidian University, Xi′an 710071, China
推荐引用方式 GB/T 7714	Yi-Ming Lin, Yuan Gao, Mao-Guo Gong, Si-Jia Zhang, Yuan-Qiao Zhang, Zhi-Yuan Li. Federated Learning on Multimodal Data: A Comprehensive Survey[J]. Machine Intelligence Research,2023,20(4):539-553.
APA	Yi-Ming Lin, Yuan Gao, Mao-Guo Gong, Si-Jia Zhang, Yuan-Qiao Zhang, Zhi-Yuan Li.(2023).Federated Learning on Multimodal Data: A Comprehensive Survey.Machine Intelligence Research,20(4),539-553.
MLA	Yi-Ming Lin, Yuan Gao, Mao-Guo Gong, Si-Jia Zhang, Yuan-Qiao Zhang, Zhi-Yuan Li."Federated Learning on Multimodal Data: A Comprehensive Survey".Machine Intelligence Research 20.4(2023):539-553.