Robust Federated Learning with Realistic Corruption

Author:

Zhao Puning,Wu Jiafei,Liu Zhe

Publisher

Springer Nature Singapore

Reference32 articles.

1. McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017)

2. Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends® Mach. Learn. 14(1–2), 1–210 (2021)

3. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 2938–2948 (2020)

4. Lyu, W., Zheng, S., Pang, L., Ling, H., Chen, C.: Attention-enhancing backdoor attacks against BERT-based models. Find. Assoc. Comput. Linguist. EMNLP 2023, 10672–10690 (2023)

5. Lyu, W., Zheng, S., Ma, T., Chen, C.: A study of the attention abnormality in Trojaned BERTs. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4727–4741 (2022)

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