FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning
Summary: FedCSS: bilevel, privacy-preserving joint client-and-sample selection for hard, informative data in FL, with meta-learning online adaptation. Convergence guarantees; outperforms baselines on five real datasets, up to 26.4% accuracy and 41.5% less communication. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Anran Li (Nanyang Technological University)
- 2. Yue Cao (Nanyang Technological University)
- 3. Jiabao Guo (Wuhan University)
- 4. Hongyi Peng (Alibaba; Nanyang Technological University)
- 5. Qing Guo (Agency for Science, Technology and Research)
- 6. Han Yu (Nanyang Technological University)
BibTeX Citation
@inproceedings{li_sigmod23,
title = {{FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning}},
author = {Li, Anran and Cao, Yue and Guo, Jiabao and Peng, Hongyi and Guo, Qing and Yu, Han},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3617332},
url = {https://dl.acm.org/doi/10.1145/3617332},
year = {2023}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,221 | Camel: Managing Data for Efficient Stream Learning | 2022 | SIGMOD | 7.6271601e-05 |
| 6,406 | Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy | 2022 | VLDB | 5.885424e-05 |
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