We present ByzFL, an open-source Python library for developing and benchmarking robust federated learning (FL) algorithms. ByzFL provides a unified and extensible framework that includes implementations of state-of-the-art robust aggregators, a suite of configurable attacks, and tools for simulating a variety of FL scenarios, including heterogeneous data distributions, multiple training algorithms, and adversarial threat models. The library enables systematic experimentation via a single JSON-based configuration file and includes built-in utilities for result visualization. Compatible with PyTorch tensors and NumPy arrays, ByzFL is designed to facilitate reproducible research and rapid prototyping of robust FL solutions. ByzFL is available at https://byzfl.epfl.ch/, with source code hosted on GitHub: https://github.com/LPD-EPFL/byzfl.
@article{arxiv.2505.24802,
title = {ByzFL: Research Framework for Robust Federated Learning},
author = {Marc González and Rachid Guerraoui and Rafael Pinot and Geovani Rizk and John Stephan and François Taïani},
journal= {arXiv preprint arXiv:2505.24802},
year = {2025}
}