Format: (yyyy)**[Conference/Journal Link]**"Paper Link"
## Surveys
1. (2021)[**[Knowledge-Based Systems]**](https://www.sciencedirect.com/journal/knowledge-based-systems) ["A Survey on Federated Learning"](https://doi.org/10.1016/j.knosys.2021.106775)

2. (2021)[**[Foundations and Trends in Machine Learning]**](https://www.nowpublishers.com/MAL) ["Advances and Open Problems in Federated Learning"](https://arxiv.org/abs/1912.04977)

3. (2023)[**[IEEE Transactions on Knowledge and Data Engineering]**](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69) ["A Survey on Federated Learning Systems: Vision, Hype, and Reality for Data Privacy and Protection"](https://ieeexplore.ieee.org/document/9599369))

## Client Heterogeneity / Non-IID
1. (2021)[**[International Conference on Machine Learning]**](https://icml.cc/Conferences/2021)["Data-Free Knowledge Distillation for Heterogeneous Federated Learning"](https://icml.cc/Conferences/2021/Schedule?showEvent=10091)
2. (2022)[**[Conference on Computer Vision and Pattern Recognition]**](https://openaccess.thecvf.com/menu)["Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning"](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.pdf)
3. (2022)[**[European Conference on Computer Vision]**](https://link.springer.com/conference/eccv)["Addressing Heterogeneity in Federated Learning via Distributional Transformation"](https://link.springer.com/chapter/10.1007/978-3-031-19839-7_11)
4. (2022)[**[IEEE International Conference on Data Mining]**](https://ieeexplore.ieee.org/xpl/conhome/1000179/all-proceedings)["Personalized Federated Learning via Heterogeneous Modular Networks"](https://ieeexplore.ieee.org/document/10027784)
5. (2023)[**[Association for the Advancement of Artificial Intelligence]**](https://aaai.org/)["Tackling Data Heterogeneity in Federated Learning with Class Prototypes"](https://ojs.aaai.org/index.php/AAAI/article/view/25891)

6. (2023)[**[Conference on Computer Vision and Pattern Recognition]**](https://openaccess.thecvf.com/menu)["DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics"](https://openaccess.thecvf.com/content/CVPR2023/html/Pi_DynaFed_Tackling_Client_Data_Heterogeneity_With_Global_Dynamics_CVPR_2023_paper.html)

## Domain Generalization
1. (2021)[**[Conference on Computer Vision and Pattern Recognition]**](https://openaccess.thecvf.com/menu)["Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space"](https://openaccess.thecvf.com/content/CVPR2021/html/Liu_FedDG_Federated_Domain_Generalization_on_Medical_Image_Segmentation_via_Episodic_CVPR_2021_paper.html)
## Semi-Supervised FL

1. (2021)[**[International Conference on Learning Representations]**](https://dblp.org/db/conf/iclr/index.html)["Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning"](https://arxiv.org/abs/2006.12097)

2. (2022)[**[Advances in Neural Information Processing Systems]**](https://proceedings.neurips.cc/)["SemiFL: Semi-supervised Federated Learning for Unlabeled Clients with Alternate Training"](https://proceedings.neurips.cc/paper_files/paper/2022/hash/71c3451f6cd6a4f82bb822db25cea4fd-Abstract-Conference.html)

3. (2022)[**[Conference on Computer Vision and Pattern Recognition]**](https://openaccess.thecvf.com/menu)["RSCFed: Random Sampling Consensus Federated Semi-supervised Learning"](https://openaccess.thecvf.com/content/CVPR2022/html/Liang_RSCFed_Random_Sampling_Consensus_Federated_Semi-Supervised_Learning_CVPR_2022_paper.html)

4. (2023)[**[IEEE Transactions on Medical Imaging]**](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=42)["Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging"](https://ieeexplore.ieee.org/document/10004993)