Clustered Federated Learning with Inference Hash Codes Based Local Sensitive Hashing

Author:

Tan Zhou,Liu Ximeng,Che Yan,Wang Yuyang

Publisher

Springer Nature Singapore

Reference38 articles.

1. Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., Pedarsani, R.: FedPAQ: a communication-efficient federated learning method with periodic averaging and quantization. In: International Conference on Artificial Intelligence and Statistics, pp. 2021–2031. PMLR (2020)

2. Dinh, C.T., Tran, N., Nguyen, J.: Personalized federated learning with Moreau envelopes. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21394–21405 (2020)

3. Li, X., Huang, K., Yang, W., Wang, S., Zhang, Z.: On the convergence of FedAvg on Non-IID data. arXiv preprint arXiv:1907.02189 (2019)

4. Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on Non-IID data silos: an experimental study. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 965–978. IEEE (2022)

5. Liang, P.P., et al.: Think locally, act globally: federated learning with local and global representations. arXiv preprint arXiv:2001.01523 (2020)

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