## Federated Self-Supervised Learning 

### 2022

| Title                                                        | Venue   | Link                                                         |
| ------------------------------------------------------------ | ------- | ------------------------------------------------------------ |
| SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision | FL-AAAI | [pdf](https://federated-learning.org/fl-aaai-2022/Papers/FL-AAAI-22_paper_36.pdf) |
| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering | ICML    | [pdf](https://arxiv.org/pdf/2205.11506.pdf), [code](https://github.com/akhilmathurs/orchestra) |
| Divergence-aware Federated Self-Supervised Learning          | ICLR    | [pdf](https://arxiv.org/pdf/2204.04385.pdf)                  |
| Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients | ICLR    | [pdf](https://niug1984.github.io/paper/lu_iclr22.pdf)        |

### 2021

| Title                                                        | Venue | Link                                                         |
| ------------------------------------------------------------ | ----- | ------------------------------------------------------------ |
| Collaborative unsupervised visual representation learning from decentralized data | ICCV  | [pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhuang_Collaborative_Unsupervised_Visual_Representation_Learning_From_Decentralized_Data_ICCV_2021_paper.pdf) |

### 2020

| Title                                                        | Venue      | Link                                                      |
| ------------------------------------------------------------ | ---------- | --------------------------------------------------------- |
| Performance optimization of federated person re-identification via benchmark analysis | MM         | [pdf](https://dl.acm.org/doi/abs/10.1145/3394171.3413814) |
| Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence | IEEE IoT-J | [pdf](https://arxiv.org/pdf/2007.13018.pdf)               |
| Federated unsupervised representation learning               | Arxiv      | [pdf](https://arxiv.org/pdf/2010.08982.pdf)               |
| Towards utilizing unlabeled data in federated learning: A survey and prospective | Arxiv      | [pdf](https://arxiv.org/pdf/2002.11545.pdf)               |
| Towards federated unsupervised representation learning       | EdgeSys    | [pdf](https://dl.acm.org/doi/abs/10.1145/3378679.3394530) |