Understanding the Impact of Data Noise in Federated Learning: [Experiments & Analysis]
Summary: Empirical study across image, video, audio, text, and tabular FL using DataNoiseGenerator. Shows FL is more noise-sensitive than centralized learning: server aggregation amplifies divergent noisy-client updates, motivating decentralized data cleaning. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Jinming Hu (University of Toronto)
- 2. Jiahao Gu (University of Toronto)
- 3. Kenta Ploch (University of Toronto)
- 4. Hao Wang (Stevens Institute of Technology)
- 5. Jingxian Wang (National University of Singapore)
- 6. Wentao Wu (Microsoft)
- 7. Qizhen Zhang (University of Toronto)
BibTeX Citation
@inproceedings{hu_sigmod26,
title = {{Understanding the Impact of Data Noise in Federated Learning: [Experiments \& Analysis]}},
author = {Hu, Jinming and Gu, Jiahao and Ploch, Kenta and Wang, Hao and Wang, Jingxian and Wu, Wentao and Zhang, Qizhen},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802124},
url = {https://dl.acm.org/doi/10.1145/3802124},
year = {2026}
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