Federated Learning: Bridging Data Privacy and Model Accuracy on JointCloud
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5663-6_20
Reference12 articles.
1. Zhang, J., Wu, Y., Pan, R.: Incentive mechanism for horizontal federated learning based on reputation and reverse auction. In: Proceedings of the Web Conference 2021, pp. 947–956 (2021)
2. Le, T.H.T., Tran, N.H., Tun, Y.K., Han, Z., Hong, C.S.: Auction based incentive design for efficient federated learning in cellular wireless networks. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2020)
3. Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Trans. Mob. Comput. 20(10), 3034–3048 (2021). https://doi.org/10.1109/TMC.2020
4. Cheng, Z., Liwang, M., Xia, X., Min, M., Wang, X., Du, X.: Auction-promoted trading for multiple federated learning services in UAV-aided networks. IEEE Trans. Veh. Technol. 71(10), 10960–10974 (2022)
5. Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. IEEE Trans. Neural Netw. Learn. Syst. (2022)
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