Lightweight and privacy-preserving hierarchical federated learning mechanism for artificial intelligence-generated image content

Author:

Wang Bingquan,Yang Fangling

Publisher

Springer Science and Business Media LLC

Reference23 articles.

1. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., Sun, L.: A comprehensive survey of ai-generated content (magic): a history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226 (2023)

2. Wu, J., Gan, W., Chen, Z., Wan, S., Lin, H.: Ai-generated content (aigc): a survey. arXiv preprint arXiv:2304.06632 (2023)

3. Xu, M. et al.: Unleashing the power of edge-cloud generative AI in mobile networks: a survey of AIGC services. arXiv:2303.16129 (2023).

4. Wang, H., Kaplan, Z., Niu, D., et al.: Optimizing federated learning on non-iid data with reinforcement learning. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 1698–1707 (2020)

5. Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429–450 (2020)

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