20241205 update

README.md

@@ -1,2 +1,30 @@
-# SlaugFL
-Code for SlaugFL
+# SlaugFL: Efficient Edge Federated Learning With Selective GAN-Based Data Augmentation
+
+This is the pytorch implementation code of the paper "[SlaugFL: Efficient Edge Federated Learning with Selective GAN-based Data Augmentation](https://ieeexplore.ieee.org/abstract/document/10521828)"
+
+## Overview
+The following figure shows an overview of our SlaugFL. The SlaugFL consists of two parts. The left part, in the dotted box, is the preparation phase which aims to obtain a trained ACGAN model. The right part shows the process of our FL collaborative training. In the collaborative training phase, we propose a dual-calibration method on the device side to improve FL performance: during the local training phase of selected devices, the local model is calibrated on the augmented data and further corrected through aligning its local class prototypes with the received p-f global class prototypes.
+
+![SlaugFL](./SlaugFL_Overview.png)
+
+We divide the implementation code into two parts: The Preparation Phase and The Collaborative Training Phase.
+
+## The Preparation Phase
+
+
+## The Collaborative Training Phase
+
+
+## Citation
+
+If you find this code is useful to your research, please consider to cite our paper.
+
+```
+@article{liu2024slaugfl,
+  title={SlaugFL: Efficient Edge Federated Learning with Selective GAN-based Data Augmentation},
+  author={Liu, Jianqi and Zhao, Zhiwei and Luo, Xiangyang and Li, Pan and Min, Geyong and Li, Huiyong},
+  journal={IEEE Transactions on Mobile Computing},
+  year={2024},
+  publisher={IEEE}
+}
+```