Wang, Jianhua; Chang, Xialoin; Rodríguez, Ricardo J.; Wang, Yixiang
Assessing Anonymous and Selfish Free-rider Attacks in Federated Learning Proceedings Article
In: Proceedings of the 2022 IEEE Symposium on Computers and Communications, pp. 6, IEEE, 2022.
Abstract | Links | BibTeX | Tags: federated learning, free-rider attack, privacy data
@inproceedings{WCRW-ISCC-22,
title = {Assessing Anonymous and Selfish Free-rider Attacks in Federated Learning},
author = {Jianhua Wang and Xialoin Chang and Ricardo J. Rodríguez and Yixiang Wang},
url = {http://webdiis.unizar.es/~ricardo/files/papers/WCRW-ISCC-22.pdf},
doi = {10.1109/ISCC55528.2022.9912903},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the 2022 IEEE Symposium on Computers and Communications},
pages = {6},
publisher = {IEEE},
abstract = {Federated Learning (FL) is a distributed learning framework and gains interest due to protecting the privacy of participants. Thus, if some participants are free-riders who are attackers without contributing any computation resources and privacy data, the model faces privacy leakage and inferior performance. In this paper, we explore and define two free-rider attack scenarios, anonymous and selfish free-rider attacks. Then we propose two methods, namely novel and advanced methods, to construct these two attacks. Extensive experiment results reveal the effectiveness in terms of the less deviation with conventional FL using the novel method, and high false positive rate to puzzle defense model using the advanced method.},
keywords = {federated learning, free-rider attack, privacy data},
pubstate = {published},
tppubtype = {inproceedings}
}
Federated Learning (FL) is a distributed learning framework and gains interest due to protecting the privacy of participants. Thus, if some participants are free-riders who are attackers without contributing any computation resources and privacy data, the model faces privacy leakage and inferior performance. In this paper, we explore and define two free-rider attack scenarios, anonymous and selfish free-rider attacks. Then we propose two methods, namely novel and advanced methods, to construct these two attacks. Extensive experiment results reveal the effectiveness in terms of the less deviation with conventional FL using the novel method, and high false positive rate to puzzle defense model using the advanced method.