Federated Learning Enhanced by Continual Learning for Common and Uncommon Features
Author:
Affiliation:
1. NEC Secure System Platform Research Laboratories
2. Kyoto University
Publisher
Japanese Society for Artificial Intelligence
Link
https://www.jstage.jst.go.jp/article/tjsai/39/3/39_39-3_A-N72/_pdf
Reference62 articles.
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3. [Briggs 20] Briggs, C., Fan, Z., and Andras, P.: Federated learning with hierarchical clustering of local updates to improve training on non-IID data, in 2020 International Joint Conference on Neural Net-works (IJCNN), pp. 1–9 (2020)
4. [Cheng 21] Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., and Yang, Q.: SecureBoost: A lossless federated learning framework, IEEE Intelligent Systems, Vol. 36, No. 6, pp. 87–98 (2021)
5. [Chu 21] Chu, K.-F. and Zhang, L.: Privacy-preserving self-taught federated learning for heterogeneous data, arXiv preprint arXiv:2102.05883 (2021)
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