Update README.md

README.md

@@ -108,11 +108,6 @@ The origin of the **data heterogeneity** phenomenon is the characteristics of us
 - **FedCAC** — [Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration](https://arxiv.org/abs/2309.11103) *ICCV 2023*
 - **PFL-DA** — [Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing](https://www.tandfonline.com/doi/full/10.1080/00401706.2022.2157882) *Technometrics 2023*
 
-  ***Other pFL***
-
-- **FedMTL (not MOCHA)** — [Federated multi-task learning](https://papers.nips.cc/paper/2017/hash/6211080fa89981f66b1a0c9d55c61d0f-Abstract.html) *NeurIPS 2017*
-- **FedBN** — [FedBN: Federated Learning on non-IID Features via Local Batch Normalization](https://openreview.net/forum?id=6YEQUn0QICG) *ICLR 2021*
-
   ***Knowledge-distillation-based pFL (more in [HtFLlib](https://github.com/TsingZ0/HtFLlib))***
 
 - **FedDistill (FD)** — [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://arxiv.org/pdf/1811.11479.pdf) *2018*
@@ -122,6 +117,11 @@ The origin of the **data heterogeneity** phenomenon is the characteristics of us
 - **FedPCL (w/o pre-trained models)** — [Federated learning from pre-trained models: A contrastive learning approach](https://proceedings.neurips.cc/paper_files/paper/2022/file/7aa320d2b4b8f6400b18f6f77b6c1535-Paper-Conference.pdf) *NeurIPS 2022* 
 - **FedPAC** — [Personalized Federated Learning with Feature Alignment and Classifier Collaboration](https://openreview.net/pdf?id=SXZr8aDKia) *ICLR 2023*
 
+  ***Other pFL***
+
+- **FedMTL (not MOCHA)** — [Federated multi-task learning](https://papers.nips.cc/paper/2017/hash/6211080fa89981f66b1a0c9d55c61d0f-Abstract.html) *NeurIPS 2017*
+- **FedBN** — [FedBN: Federated Learning on non-IID Features via Local Batch Normalization](https://openreview.net/forum?id=6YEQUn0QICG) *ICLR 2021*
+
 ## Datasets and scenarios (updating)
 
 We support 3 types of scenarios with various datasets and move the common dataset splitting code into `./dataset/utils` for easy extension. If you need another data set, just write another code to download it and then use the [utils](https://github.com/TsingZ0/PFLlib/tree/master/dataset/utils).