RoFL: A Robust Federated Learning Scheme Against Malicious Attacks

Author:

Wei Ming,Liu XiaofanORCID,Ren WeiORCID

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Aledhari, M., Razzak, R., Parizi, R.M., Saeed, F.: Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 8, 140699–140725 (2020)

2. Xiao, R., Ren, W., Zhu, T., Choo, K.-K.R.: A mixing scheme using a decentralized signature protocol for privacy protection in bitcoin blockchain. IEEE Trans. Dependable Secure Comput. 18(4), 1793–1803 (2019)

3. Li, B., Liang, R., Zhou, W., Yin, H., Gao, H., Cai, K.: LBS meets blockchain: an efficient method with security preserving trust in SAGIN. IEEE Internet Things J. 9(8), 5932–5942 (2021)

4. Liu, Y., et al.: A blockchain-based decentralized, fair and authenticated information sharing scheme in zero trust Internet-of-Things. IEEE Trans. Comput. 72(2), 501–512 (2023)

5. 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. PMLR (2017)

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