A Federated Learning System with Enhanced Feature Extraction for Human Activity Recognition

Impact Factor:8.139

DOI number:10.1016/j.knosys.2021.107338

Affiliation of Author(s):Southwest Jiaotong Univ, Sch Comp & Artificial Intelligence

Journal:Knowledge-Based Systems

Key Words:Deep learning,Feature extraction,Federated learning,Human activity recognition,Wearable sensors

Abstract:With the rapid growth of mobile devices, wearable sensor-based human activity recognition (HAR) has become one of the hottest topics in the Internet of Things. However, it is challenging for traditional approaches to achieving high recognition accuracy while protecting users' privacy and sensitive information. To this end, we design a federated learning system for HAR (HARFLS). Based on the Federated Averaging method, HARFLS enables each user to handle its activity recognition task safely and collectively. However, the recognition accuracy largely depends on the system's feature extraction ability. To capture sufficient features from HAR data, we design a perceptive extraction network (PEN) as the feature extractor for each user. PEN is mainly composed of a feature network and a relation network. The feature network, based on a convolutional block, is responsible for discovering local features from the HAR data while the relation network, a combination of long short term memory (LSTM) and attention mechanism, focuses on mining global relationships hidden in the data. Four widely used datasets, i.e., WISDM, UCI_HAR 2012, OPPORTUNITY, and PAMAP2, are used for performance evaluation. Experimental results demonstrate that PEN outperforms 14 existing HAR algorithms on these datasets in terms of the F1-score; HARFLS with PEN obtains better recognition results on the WISDM and PAMAP2 datasets, compared with 11 existing federated learning systems with various feature extraction structures. (C) 2021 Elsevier B.V. All rights reserved.With the rapid growth of mobile devices, wearable sensor-based human activity recognition (HAR) has become one of the hottest topics in the Internet of Things. However, it is challenging for traditional approaches to achieving high recognition accuracy while protecting users' privacy and sensitive information. To this end, we design a federated learning system for HAR (HARFLS). Based on the Federated Averaging method, HARFLS enables each user to handle its activity recognition task safely and collectively. However, the recognition accuracy largely depends on the system's feature extraction ability. To capture sufficient features from HAR data, we design a perceptive extraction network (PEN) as the feature extractor for each user. PEN is mainly composed of a feature network and a relation network. The feature network, based on a convolutional block, is responsible for discovering local features from the HAR data while the relation network, a combination of long short term memory (LSTM) and attention mechanism, focuses on mining global relationships hidden in the data. Four widely used datasets, i.e., WISDM, UCI_HAR 2012, OPPORTUNITY, and PAMAP2, are used for performance evaluation. Experimental results demonstrate that PEN outperforms 14 existing HAR algorithms on these datasets in terms of the F1-score; HARFLS with PEN obtains better recognition results on the WISDM and PAMAP2 datasets, compared with 11 existing federated learning systems with various feature extraction structures. (C) 2021 Elsevier B.V. All rights reserved.

Co-author:Zhiwen Xiao,Xin Xu,Huanlai Xing*,Fuhong Song,Xinhan Wang,Bowen Zhao

Document Code:10.1016/j.knosys.2021.107338

Volume:229

ISSN No.:0950-7051

Translation or Not:no

Date of Publication:2021-10-11

Included Journals:SCI