Publisher
Springer Nature Singapore
Reference18 articles.
1. Konečný, J., McMahan, H.B., Yu, F.X., et al.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)
2. Xu, X., Deng, H.H., Chen, T., et al.: Federated cross learning for medical image segmentation. In: Medical Imaging with Deep Learning, pp. 1441–1452. PMLR (2024)
3. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) Automata, Languages and Programming. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1
4. Hu, R., Guo, Y., Gong, Y.: Federated learning with sparsified model perturbation: improving accuracy under client-level differential privacy. IEEE Trans. Mob. Comput. (2023)
5. Wei, K., Li, J., Ding, M., et al.: User-level privacy-preserving federated learning: analysis and performance optimization. IEEE Trans. Mob. Comput. 21(9), 3388–3401 (2021)