[GitHub]

Collect some Federated Noise Learning papers.

Please give me a ⭐star if you find it useful (❁´◡`❁).

If you find some overlooked papers, please open issues or pull requests(recommended), following the Contributing section.

Last Update: Dec 27, 2023 16:03:46

• [RoFL] Robust Federated Learning with Noisy Labels (IEEE Intelligent Systems) [PDF] [CODE]

• [FedNoRo] FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity (IJCAI) [PDF] [CODE]

• [FedCorr] FedCorr: Multi-Stage Federated Learning for Label Noise Correction (CVPR) [PDF] [CODE]
• [FEDLSR] Towards Federated Learning against Noisy Labels via Local Self-Regularization (CIKM) [PDF] [CODE]

• [Mixup] Mixup: Beyond empirical risk minimization (ICLR) [PDF] [CODE]

• [SCE] Symmetric Cross Entropy for Robust Learning with Noisy Labels (ICCV) [PDF]

• [GCE] Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels (NeurIPS) [PDF]

• [MAE] Robust Loss Functions under Label Noise for Deep Neural Networks (AAAI) [PDF]

• Unsupervised Label Noise Modeling and Loss Correction (ICML) [PDF]

• [Co-teaching] Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels (NeurIPS) [PDF] [CODE]

You can contribute to this project by opening an issue or creating a pull request on GitHub.

Add paper to the papers.yaml file with the following format:

- title: "Communication-Efficient Learning of Deep Networks from Decentralized Data"
  abbr: FedAvg
  year: 2016
  conf: AISTAT
  links:
    PDF: https://arxiv.org/abs/1602.05629.pdf
    GitHub:

@misc{awesomeafl,
    title = {awesome-asyncrhonous-federated-learning},
    author = {miku8miku},
    year = {2023},
    howpublished = {\\url{https://github.com/beiyuouo/awesome-asynchronous-federated-learning}
}