Project dependencies (such as torch and flwr) are defined in ./PRIMES_FLrequirements.txt. To install those dependencies and manage your virtual environment, use pip, but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
To run experiments using a select strategy, uncomment the strategy among {PrimesStrategy, ClippingStrategy, NormalFedAvg} you wish to use in ./pytorch_quickstart/server.py and uncomment the rest.
Then, configure the number of clients in pytorch_quickstart/run.sh. Note that there are three configurations: --dataset (int: {1 (for MNIST), 2 (for CIFAR-10)}), --fraction (int: larger = smaller data size for client), and --corruption (float: [0, 1], determines proportion of that client's data that is corrupted/zeroed out).
After doing these, run:
python primes_server.py
Then in another terminal, run:
./pytorch_quickstart/run.sh
to see the federated learning experiment begin.
For 10 rounds accuracy overtime on MNIST, change the total rounds in server.py to 10, set 50 clients in run.sh, with 10 clients in each of these corruption levels: 0.1, 0.3, 0.5, 0.7, 0.9. Set the fraction to 50 (so each client has 1/50 of the total data distribution). Set dataset to 1 for MNIST. After these are ready, uncomment the desired strategy in server.py.
For other tests, set the total training rounds in server.py to 50. For running CIFAR-10, set dataset to 2 in run.sh.
For the bad/good client ratio test, set 50 clients in run.sh, with varying ratios of clients in these corruption levels: 0.6 (bad clients), 0.1 (good clients).