The Price of Labelling: A Two-Phase Federated Self-learning Approach

Author:

Aladwani Tahani,Parambath Shameem Puthiya,Anagnostopoulos Christos,Deligianni Fani

Publisher

Springer Nature Switzerland

Reference36 articles.

1. Bian, J., Fu, Z., Xu, J.: FedSEAL: semi-supervised federated learning with self-ensemble learning and negative learning (2021). Preprint arXiv:2110.07829

2. Che, L., Long, Z., Wang, J., Wang, Y., Xiao, H., Ma, F.: FedTriNet: A pseudo labeling method with three players for federated semi-supervised learning

3. Chiu, T.-C., Shih, Y.-Y., Pang, A.-C., Wang, C.-S., Weng, W., Chou, C.-T.: Semisupervised distributed learning with non-IID data for AIoT service platform. IEEE Internet Things J. 7(10), 9266–9277 (2020)

4. Dai, Y., Chen, Z., Li, J., Heinecke, S., Sun, L., Ran, X.: Tackling data heterogeneity in federated learning with class prototypes. In AAAI 37(6), 7314–7322 (2023)

5. Di, Z., Zhu, Z., Wang, X.E., Liu, Y.: Federated Learning with Openset Noisy Labels (2022)

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