FLAP: Federated learning by data-agnostic pruning

THIS REPO IS IMPLEMENTED BASED ON A PUBLIC PROJECT https://github.com/pps-lab/fl-analysis

The configuration below is copied from the fl-analysis project, please also credit the original repo. for any reproducing scientific work.

Set up the execution

Step 1: Configure your Python (3.7) + tensorflow 2.5.0 environment

conda activate <environment_name>

Step 2: Install the required packages by "pipenv install" and activate it by launching a shell

pipenv shell

Step 3: Review the configuration file "config.yml" and run the code by calling command below:

python -m src.main -c config.yml

Usage

The configuration of the framework is specified in a config file in YAML format. A minimal example of a config is shown below.

environment:
  num_clients: 3383
  num_selected_clients: 30
  num_malicious_clients: 0
  experiment_name: "Sample run without attackers"

server:
  num_rounds: 80
  num_test_batches: 5
  aggregator:
    name: FedAvg
  global_learning_rate: -1

client:
  clip:
    type: l2
    value: 10
  model_name: resnet18
  benign_training:
    num_epochs: 2
    batch_size: 24
    optimizer: Adam
    learning_rate: 0.001

dataset:
  dataset: femnist
  data_distribution: nonIID

The full specification of the supported config options can be found here Some example config files can be find in train_configs.

Output

Basic training progress is sent to standard output. More elaborate information is stored in an output folder. The directory location can be specified through the XXX option. By default, its ... . The framework stores progress in tfevents, which can be viewed using Tensorboard, e.g.,

tensorboard --logdir ./experiments/{experiment_name}

License

This project's code is distributed under the MIT License. See LICENSE for more information.