Clustered Federated Learning Framework with Acceleration Based on Data Similarity

Author:

Gao ZhiPeng,Xiong ZiJian,Zhao Chen,Feng FuTeng

Publisher

Springer Nature Singapore

Reference15 articles.

1. Mukhopadhyay, S.C., Tyagi, S.K.S., Suryadevara, N.K., Piuri, V., Scotti, F., Zeadally, S.: Artificial intelligence-based sensors for next generation IoT applications: a review. IEEE Sens. J. 21(22), 24920–24932 (2021)

2. Shah, R., Chircu, A.: IoT and AI in healthcare: a systematic literature review. Issues Inf. Syst. 19(3) (2018)

3. Kankanhalli, A., Charalabidis, Y., Mellouli, S.: A research agenda, IoT and AI for smart government (2019)

4. McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)

5. 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)

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