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- W4297154057 abstract "Federated learning (FL) provides a new learning paradigm without compromising client privacy to solve contradictions between privacy-preserving concerns and big data requirements. Clients do not need to transmit personal data and train locally in FL. However, there is a challenge against backdoor attacks on client sides. To overcome this challenge, we proposed a weight-based federated aggregation (WFA) that strengthens regular models and decreases corrupted modes for the global model, which leads to high robustness to resist backdoor attacks. We get weights by minimizing the sum of deviations between client models and the geometric median of client models. To avoid the impact of the curse of dimensionality, we use a nonlinear principal component analysis method to reduce the dimension of received model data before computing deviations in WFA. Extensive experiments demonstrate that WFA generally has good robustness in defeating backdoor attacks, and the robust aggregation approach of WFA is agnostic to the level of corruption." @default.
- W4297154057 created "2022-09-27" @default.
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- W4297154057 date "2022-01-01" @default.
- W4297154057 modified "2023-09-26" @default.
- W4297154057 title "Mitigating the Backdoor Attack by a Weight-Based Federated Aggregation" @default.
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- W4297154057 doi "https://doi.org/10.1007/978-981-19-6203-5_20" @default.
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