Publisher
Zhejiang University Press
Reference23 articles.
1. Cao QM, Zhang X, Zhang YS, et al., 2021. Layered model aggregation based federated learning in mobile edge networks. IEEE/CIC Int Conf on Communications in China, p.1–6. https://doi.org/10.1109/ICCC52777.2021.9580403
2. Chen MZ, Yang ZH, Saad W, et al., 2021. A joint learning and communications framework for federated learning over wireless networks. IEEE Trans Wirel Commun, 20(1):269–283. https://doi.org/10.1109/TWC.2020.3024629
3. Deng YH, Lyu F, Ren J, et al., 2021. Share: shaping data distribution at edge for communication-efficient hierarchical federated learning. IEEE 41st Int Conf on Distributed Computing Systems, p.24–34. https://doi.org/10.1109/ICDCS51616.2021.00012
4. Dinh CT, Tran NH, Nguyen MNH, et al., 2021. Federated learning over wireless networks: convergence analysis and resource allocation. IEEE/ACM Trans Netw, 29(1):398–409. https://doi.org/10.1109/TNET.2020.3035770
5. Fraboni Y, Vidal R, Kameni L, et al., 2021. Clustered sampling: low-variance and improved representativity for clients selection in federated learning. Proc 38th Int Conf on Machine Learning, p.3407–3416.