ByzFL Documentation

Welcome to the official documentation of ByzFL, developed by DCL at EPFL and WIDE at INRIA Rennes!

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What is ByzFL?

ByzFL is a Python library for Byzantine-resilient Federated Learning. It is fully compatible with both PyTorch tensors and NumPy arrays, making it versatile for a wide range of machine learning workflows.

Key Features

1. Robust Aggregators and Pre-Aggregators:
   • Aggregate gradients robustly while mitigating the impact of Byzantine participants.
2. Byzantine Attacks:
   • Simulate and evaluate different attack strategies to test resilience.
3. Machine Learning Pipelines:
   • Train and benchmark robust aggregation schemes and attack implementations seamlessly.

The exact implementations of these modules (aggregators, attacks, and pipeline) can be found in the byzfl/ directory.

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Installation

Install the ByzFL library using pip:

pip install byzfl

After installation, the library is ready to use. Here’s a quick example of how to use the Trmean robust aggregator and the SignFlipping Byzantine attack:

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Quick Start Example

import byzfl
import numpy as np

# Number of Byzantine participants
f = 1

# Honest vectors
honest_vectors = np.array([[1., 2., 3.],
                           [4., 5., 6.],
                           [7., 8., 9.]])

# Initialize and apply the attack
attack = byzfl.SignFlipping()
byz_vector = attack(honest_vectors)

# Create f identical attack vectors
byz_vectors = np.tile(byz_vector, (f, 1))

# Concatenate honest and Byzantine vectors
all_vectors = np.concatenate((honest_vectors, byz_vectors), axis=0)

# Initialize and perform robust aggregation
aggregate = byzfl.Trmean(f=f)
result = aggregate(all_vectors)
print("Aggregated result:", result)

Output:

Aggregated result: [2.5 3.5 4.5]

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Learn More

Explore the key components of ByzFL:

• Aggregators
  Learn about robust aggregation methods.
• Attacks
  Discover Byzantine attack implementations.
• Pipeline
  Build and benchmark your models.

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License

ByzFL is open-source and distributed under the MIT License.

Contributions are welcome!