Conclusion
The letter gives an effective defense paradigm to defend against local model poisoning attack in FL without auxiliary dataset, which further enhances the robust of Byzantine-robust aggregation rules to local model poisoning attack. The experiment results show that our defense scheme can obtain a better detection performance and take less detection time in local model poisoning attack. More technical details please refer to supplementary material.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (Grand Nos. 11901579, 11801564).
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The supporting information is available online at journal.hep.com.cn and link.springer.com.
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Lu, S., Li, R., Chen, X. et al. Defense against local model poisoning attacks to byzantine-robust federated learning. Front. Comput. Sci. 16, 166337 (2022). https://doi.org/10.1007/s11704-021-1067-4
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DOI: https://doi.org/10.1007/s11704-021-1067-4