IEICE TRANSACTIONS on Information
Layer-Based Communication-Efficient Federated Learning with Privacy Preservation
Summary :
In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.2 pp.256-263
- Publication Date
- 2022/02/01
- Publicized
- 2021/09/28
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021BCP0006
- Type of Manuscript
- Special Section PAPER (Special Section on Blockchain Systems and Applications)
- Category
Authors
Zhuotao LIAN
University of Aizu
Weizheng WANG
City University of Hong Kong
Huakun HUANG
Guangzhou University
Chunhua SU
University of Aizu
Keyword
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Zhuotao LIAN, Weizheng WANG, Huakun HUANG, Chunhua SU, "Layer-Based Communication-Efficient Federated Learning with Privacy Preservation" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 2, pp. 256-263, February 2022, doi: 10.1587/transinf.2021BCP0006.
Abstract: In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021BCP0006/_p
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@ARTICLE{e105-d_2_256,
author={Zhuotao LIAN, Weizheng WANG, Huakun HUANG, Chunhua SU, },
journal={IEICE TRANSACTIONS on Information},
title={Layer-Based Communication-Efficient Federated Learning with Privacy Preservation},
year={2022},
volume={E105-D},
number={2},
pages={256-263},
abstract={In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.},
keywords={},
doi={10.1587/transinf.2021BCP0006},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Layer-Based Communication-Efficient Federated Learning with Privacy Preservation
T2 - IEICE TRANSACTIONS on Information
SP - 256
EP - 263
AU - Zhuotao LIAN
AU - Weizheng WANG
AU - Huakun HUANG
AU - Chunhua SU
PY - 2022
DO - 10.1587/transinf.2021BCP0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2022
AB - In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.
ER -
English
