The code was mainly re-written based on a distinguished and excellent source code from S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, "Adaptive federated learning in resource constrained edge computing systems," IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205 – 1221, Jun. 2019.
The code runs on Python 3 with Tensorflow version 1 (>=1.13). To install the dependencies, run
pip3 install -r requirements.txt
Then, download the datasets manually and put them into the datasets folder.
- For MNIST dataset, download from http://yann.lecun.com/exdb/mnist/ and put the standalone files into
datasets/mnist. - For CIFAR-10 dataset, download from https://www.cs.toronto.edu/~kriz/cifar.html, extract the standalone
*.binfiles and put them intodatasets/cifar-10-batches-bin.
To test the code:
- Run
server.pyand wait until you seeWaiting for incoming connections...in the console output. - Run 3 parallel instances of
client2.pyon the same machine as the server. - You will see console outputs on both the server and clients indicating message exchanges. The code will run for a few minutes before finishing.
All configuration options are given in config.py which also explains the different setups that the code can run with.
The results are saved in the results folder.
Currently, the supported datasets are MNIST and CIFAR-10, and the supported models are SVM and CNN. The code can be extended to support other datasets and models too.
This code was written based on a distingushed work of Shiqiang Wang and Tiffany Tuor.