Domination Based Federated Learning Algorithm

Author:

Chen Yikuan,Li Liang,Gao WeiORCID

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Fallah, A., Mokhtari, A., Ozdaglar, A.: Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (2020)

2. Ghosh, A., Chung, J., Yin, D., Ramchandran, K.: An efficient framework for clustered federated learning. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (2020)

3. Lam, M., Wei, G., Brooks, D., Reddi, V.J., Mitzenmacher, M.: Gradient disaggregation breaking privacy in federated learning by reconstructing the user participant matrix. In: Proceedings of the 38th International Conference on Machine Learning, PMLR, vol. 139 (2021)

4. Collins, L., Hassani, H., Mokhtari, A., Shakkottai, S.: Exploiting shared representations for personalized federated learning. In: Proceedings of the 38th International Conference on Machine Learning, PMLR, vol. 139 (2021)

5. Hu, R., Guo, Y.X., Li, H.N., Pei, Q.Q., Gong, Y.M.: Personalized federated learning with differential privacy. IEEE Internet Things J. 7(10), 9530–9539 (2020)

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