Updated formatting, fixed spelling errors

README.md

@@ -1,44 +1,39 @@
 # Differentially Private Federated Learning: A client-level perspective
 
-Description:
-============
-Federated Learning is a privacy preserving decentralized learning protocol introduced my Google. Multiple clients jointly learn a model without data centralization. Centralization is pushed from data space to parameter space: https://research.google.com/pubs/pub44822.html [1].
+## Description:
+Federated Learning is a privacy preserving decentralized learning protocol introduced by Google. Multiple clients jointly learn a model without data centralization. Centralization is pushed from data space to parameter space: https://research.google.com/pubs/pub44822.html [1].
 Differential privacy in deep learning is concerned with preserving privacy of individual data points: https://arxiv.org/abs/1607.00133 [2].
 In this work we combine the notion of both by making federated learning differentially private. We focus on preserving privacy for the entire data set of a client. For more information, please refer to: https://arxiv.org/abs/1712.07557v2.
 
 This code simulates a federated setting and enables federated learning with differential privacy. The privacy accountant used is from https://arxiv.org/abs/1607.00133 [2]. The files: accountant.py, utils.py, gaussian_moments.py are taken from: https://github.com/tensorflow/models/tree/master/research/differential_privacy
 
 Note that the privacy agent is not completely set up yet (especially for more than 100 clients). It has to be specified manually or otherwise parameters 'm' and 'sigma' need to be specified.
 
-Requirements
-============
-- Tensorflow 1.4.1
-- MNIST data-set (link)[http://yann.lecun.com/exdb/mnist/]
+## Requirements
+- [Tensorflow 1.4.1](https://www.tensorflow.org/)
+- [MNIST data-set](http://yann.lecun.com/exdb/mnist/)
 
-Download and Installation
-==========================
-1- If not already done, install Tensorflow 1.4.1
+## Download and Installation
+1. Install Tensorflow 1.4.1
+2  [Download the files as a ZIP archive](archive/master.zip), or you can [clone the repository](https://help.github.com/articles/cloning-a-repository/) to your local hard drive.
 
-2- Downlad the directory this README is part of 
+3. Change to the directory of the download, If using macOS, simply run: 
+    ```bash
+    bash RUNME.sh
+    ```
+    This will download the [MNIST data-sets](http://yann.lecun.com/exdb/mnist/), create clients and getting started. 
+
+For more information on the individual functions, please refer to their doc strings.  
 
-3- If using macOS, simply run: 
-```bash
-bash RUNME.sh
-```
-to download the MNIST data-sets (link)[http://yann.lecun.com/exdb/mnist/], create clients and getting started. For more information on the individual functions, please refer to their doc strings.  
-
-Known Issues
-============
+## Known Issues
 No issues known
 
 
-How to obtain support
-=====================
-Please email the authors for any questions regarding the publication and this code. Note that this project is provided "as-is" and any bug reports are not guaranteed to be fixed.
+## How to obtain support
+Please email the authors for any questions regarding the publication and this code.  Note that this project is provided "as-is" and any bug reports are not guaranteed to be fixed.
 
 
-How to Cite
-===========
+## Citations
 If you use this code or the pretrained models in your research,
 please cite:
 
@@ -58,14 +53,12 @@ archivePrefix = "arXiv",
 }
 ```
 
-Reference(s)
-===========
-[1] H. Brendan McMahan et al., Communication-Efficient Learning of Deep Networks from Decentralized Data, 2017, http://arxiv.org/abs/1602.05629.
+## References
+- H. Brendan McMahan et al., Communication-Efficient Learning of Deep Networks from Decentralized Data, 2017, http://arxiv.org/abs/1602.05629.
 
-[2] Martin Abadi et al., Deep Learning with Differential Privacy, 2016, https://arxiv.org/abs/1607.00133.
+- Martin Abadi et al., Deep Learning with Differential Privacy, 2016, https://arxiv.org/abs/1607.00133.
 
 
-License
-=======
+## License
 
-This project is licensed under SAP Sample Code License Agreement except as noted otherwise in the LICENSE file.
+This project is licensed under SAP Sample Code License Agreement except as noted otherwise in the [LICENSE file](LICENSE.md).