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- W4313142254 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." @default.
- W4313142254 created "2023-01-06" @default.
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- W4313142254 date "2022-06-30" @default.
- W4313142254 modified "2023-10-03" @default.
- W4313142254 title "Assessing Anonymous and Selfish Free-rider Attacks in Federated Learning" @default.
- W4313142254 doi "https://doi.org/10.1109/iscc55528.2022.9912903" @default.
- W4313142254 hasPublicationYear "2022" @default.
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