计算机科学
原始数据
架空(工程)
数据建模
数据挖掘
机器学习
特征(语言学)
数据传输
人工智能
数据库
语言学
哲学
程序设计语言
计算机网络
操作系统
作者
Hao Zhang,Qingying Hou,Tingting Wu,Siyao Cheng,Jie Liu
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-12-15
卷期号:10 (24): 22530-22541
被引量:1
标识
DOI:10.1109/jiot.2023.3303889
摘要
With the rapid growth of the number of devices generating and collecting data, dispersion becomes an important feature of data in Internet of Things. Federated learning (FL) provides a feasible way to mine information in such distributed data. It involves training machine learning models over multiple distributed participants without raw data transmission. However, due to the data heterogeneity among participants, the performance of the FL model degrades dramatically. Currently, improved methods mainly reduce data heterogeneity from the perspective of modifying the process of model training, which usually have problems, such as high-resource consumption or the need for auxiliary data. In this article, we enhance FL model from another perspective, focusing on data rather than model training. We reduce data heterogeneity by enhancing the trained local data to improve FL performance. Specifically, we propose an FL method based on data augmentation (abbreviated as FedM-UNE), implementing the classic data augmentation method MixUp in federated scenarios without transferring raw data. Furthermore, in order to adapt this method to regression tasks, we first modify MixUp by bilateral neighborhood expansion (MixUp-BNE), and then propose a federated data augmentation method named FedM-BNE based on it. Compared with the conventional FL method, both FedM-UNE and FedM-BNE increase negligible overhead. To demonstrate the effectiveness, we conduct exhaustive experiments on six data sets employing a variety of loss functions. The results indicate that FedM-UNE and FedM-BNE consistently improve the performance of the FL model. Moreover, our methods are compatible with existing FL enhancements, which yield further improvements in performance.
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