A. Kumar, V. Khimani, D. Chatzopoulos and P. Hui, "FedClean: A Defense Mechanism against Parameter Poisoning Attacks in Federated Learning," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 4333-4337, doi: 10.1109/ICASSP43922.2022.9747497.
FedClean : a defense mechanism against parameter poisoning attacks in federated learning
|Author:||Kumar, Abhishek1; Khimani, Vivek2; Chatzopoulos, Dimitris3;|
1University of Oulu
3University College Dublin
4University of Helsinki
5Hong Kong University of Science and Technology
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022101161615
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-10-11
In Federated learning (FL) systems, a centralized entity (server), instead of access to the training data, has access to model parameter updates computed by each participant independently and based solely on their samples. Unfortunately, FL is susceptible to model poisoning attacks, in which malicious or malfunctioning entities share polluted updates that can compromise the model’s accuracy. In this study, we propose FedClean, an FL mechanism that is robust to model poisoning attacks. The accuracy of the models trained with the assistance of FedClean is close to the one where malicious entities do not participate.
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
|Pages:||4333 - 4337|
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
IEEE International Conference on Acoustics, Speech and Signal Processing
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
Thanks to the Academy of Finland for funding.
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