FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning-Reference-Cited by-同舟云学术

FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning

Published:2024-08-24 Issue: Volume:2022 Page:3299-3310
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Zihui¹^ORCID,Wang Zheng¹^ORCID,Lyu Lingjuan²^ORCID,Peng Zhaopeng¹^ORCID,Yang Zhicheng¹^ORCID,Wen Chenglu¹^ORCID,Yu Rongshan³^ORCID,Wang Cheng¹^ORCID,Fan Xiaoliang¹^ORCID

Affiliation:

1. Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen, China

2. Sony AI, Zurich, Swaziland

3. Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, National University of Singapore, Xiamen, Singapore

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671748

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5. Junyuan Hong, Haotao Wang, Zhangyang Wang, and Jiayu Zhou. 2022. Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization. In In Proceedings of the International Conference on Learning Representations.