FedXplore - extensible framework for federated learning

Table of contents

0. Running Experiments -- Run the command to get reproducibility
1. Quickstart -- Follow the instructions and get the result!
2. C4 notation -- Context Container Component Code scheme.
3. Federated Method Explaining -- Get the basis and write your own method
4. Config Explaining -- See allowed optionalization
5. Attacks -- Get the basis and write custom attack

🚀 Quickstart Guide

📋 Prerequisites

1. Install dependencies

python -m venv venv
source venv/bin/activate
pip install -e .

⚙️ Experiment Setups

🔄 Standard Federated Averaging on CIFAR-10

python src/train.py \
  training_params.batch_size=32 \
  federated_params.print_client_metrics=False \
  training_params.device_ids=[0] \
  > test_run_fedavg_cifar.txt

device_ids controls the GPU number (if there are several GPUs on the machine). You can specify multiple ids, then the training will be evenly distributed across the specified devices.

Additionally, manager.batch_size client processes will be created. To forcefully terminate the training, kill any of the processes.

🌪️ Heterogeneous CIFAR10 Experiment

Dirichlet Partition with $\alpha=0.1$ (strong heterogeneity)

python src/train.py \
  training_params.batch_size=32 \
  federated_params.print_client_metrics=False \
  train_dataset.alpha=0.1 \
  federated_params.amount_of_clients=100 \
  > test_run_fedavg_cifar_dirichlet_strong_heterogeneity_100_clients.txt

Uniform Distribution ($\alpha=1000$) with various amount_of_clients

dataset.alpha=1000 \
federated_params.amount_of_clients=42 \

🦠 Byzantine Attacks

FedAvg with Label Flipping Attack

python src/train.py \
  training_params.batch_size=32 \
  federated_params.print_client_metrics=False \
  federated_params.clients_attack_types=label_flip \
  federated_params.prop_attack_clients=0.5 \
  federated_params.attack_scheme=constant \
  federated_params.prop_attack_rounds=1.0 \
  > test_run_fedavg_cifar_label_flip_half_byzantines.txt