Skip to content
forked from dhitaj/FedComm

FedComm: Federated learning as a medium for covert communication

Notifications You must be signed in to change notification settings

briland/FedComm

 
 

Repository files navigation

FedComm

Experiments are produced on MNIST, CIFAR-10, and WikiText-2 datasets.

Setting up the environment

  • Install the requirements.
pip install -r requirements.txt

Data

  • Download the respective datasets and put them under 'data/' directory.

Running the experiments

  • To run the FedComm experiment:
python fedcomm.py

Setting the experiment parameters

To run the experiments in different conditions change the parameters in config.json file.

Federated Learning Parameters

  • num_users:Number of total users that have signed up for collaborating. (Default is 100).
  • frac: Fraction of users to be used for federated updates. Default is 1.0 (i.e., 100% participation).
  • epochs: Number of global training epochs. Default is 1000.
  • dataset: Default: 'mnist'. Options: 'mnist', 'cifar10', 'wiki'.

Message transmission parameters:

  • senders: Fraction of participants in the federated learning scheme that will act as senders. Default: 0.1 (i.e., 10% of the participants).
  • payload: The extension of the payload file (under payloads/ directory. Default 'txt', Options: 'txt', 'png'.
  • injection: The FL global round when the senders should start transmitting the message. Default 10.
  • stealthy: The level of stealthiness of the senders. Default 'non', Options: 'non', 'inter', 'full'.
  • run_name: A name given to the particular run. It will create a directory structure where it will store model checkpoints, extracted payloads, train accuracy and loss values.

About

FedComm: Federated learning as a medium for covert communication

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%