This repo includes four new real-world human activity recognition (HAR) datasets collected under federated learning settings, which first appear at the MobiSys 2021 paper: ClusterFL: A Similarity-Aware Federated Learning System for Human Activity Recognition .
The first dataset is a large-scale dataset collected using an Android App in a crowdsourcing manner. The other three are collected in indoor environments.
The four datasets are publicly available in the current repository. Each dataset is accompanied by a Python file "data_pre.py" for preprocessing and loading each node's data separately in federated learning. The data and processing file are compressed to a ".zip" file for each dataset. Please click the following links for more detail descriptions and downloading each dataset.
If you find the datasets useful for your research, please cite this paper:
@inproceedings{ouyang2021clusterfl,
title={ClusterFL: a similarity-aware federated learning system for human activity recognition},
author={Ouyang, Xiaomin and Xie, Zhiyuan and Zhou, Jiayu and Huang, Jianwei and Xing, Guoliang},
booktitle={Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services},
pages={54--66},
year={2021}
}