FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification

Jiang et al. "FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70352-2_11

[Jiang et al. "FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/jiang2024ecmlpkdd-fedar/) doi:10.1007/978-3-031-70352-2_11

@inproceedings{jiang2024ecmlpkdd-fedar,
  title     = {{FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification}},
  author    = {Jiang, Chutian and Zhou, Hansong and Zhang, Xiaonan and Chakraborty, Shayok},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2024},
  pages     = {178-196},
  doi       = {10.1007/978-3-031-70352-2_11},
  url       = {https://mlanthology.org/ecmlpkdd/2024/jiang2024ecmlpkdd-fedar/}
}