Robust Personalized Federated Learning with Sparse Penalization

Author:

Liu Weidong1,Mao Xiaojun2ORCID,Zhang Xiaofei3ORCID,Zhang Xin4

Affiliation:

1. School of Mathematical Sciences, MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China

2. School of Mathematical Sciences, Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai, China

3. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China

4. Department of Statistics, Iowa State University, Ames, IA

Funder

NSFC

Shanghai Rising-Star Program

CAST

Publisher

Informa UK Limited

Reference47 articles.

1. Arivazhagan M. G. Aggarwal V. Singh A. K. and Choudhary S. (2019) “Federated Learning with Personalization Layers ” arXiv preprint arXiv:1912.00818.

2. A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion

3. “Distributed High-Dimensional Regression Under a Quantile Loss Function,”;Chen X.;The Journal of Machine Learning Research,2020

4. Choi, S., Jung, S., Yun, H., Kim, J. T., Kim, S., and Choo, J. (2021), “Robustnet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11580–11590.

5. Dai, R., Shen, L., He, F., Tian, X., and Tao, D. (2022), “Dispfl: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training,” in International Conference on Machine Learning, pp. 4587–4604, PMLR.

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