ByzFL: Research Framework for Robust Federated Learning

• URL: http://arxiv.org/abs/2505.24802v1
• Date: Fri, 30 May 2025 17:08:15 GMT
• Title: ByzFL: Research Framework for Robust Federated Learning
• Authors: Marc González, Rachid Guerraoui, Rafael Pinot, Geovani Rizk, John Stephan, François Taïani,
• Abstract summary: We present ByzFL, an open-source library for developing and robust benchmarking learning (FL) algorithms.<n>ByzFL provides a unified framework that includes implementations of state-of-the-art robust aggregators.<n>The library enables systematic experimentation via a single-based configuration file and includes built-in utilities for result visualization.
• Score: 11.23722364748134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead [75.87007729801304]
  SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead.<n>To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters.<n>Global thresholds are used to update model parameters by extracting aggregated parameter importance.
  arXiv  Detail & Related papers  (2024-06-01T13:10:35Z)
• pfl-research: simulation framework for accelerating research in Private Federated Learning [6.421821657238535]
  pfl-research is a fast, modular, and easy-to-use Python framework for simulating Federated learning (FL)<n>It supports setups, PyTorch, and non-neural network models, and is tightly integrated with state-of-the-art algorithms.<n>We release a suite of benchmarks that evaluates an algorithm's overall performance on a diverse set of realistic scenarios.
  arXiv  Detail & Related papers  (2024-04-09T16:23:01Z)
• NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients [44.89061671579694]
  Federated learning (FL) enables distributed training while preserving data privacy, but stragglers-slow or incapable clients-can significantly slow down the total training time and degrade performance. We propose nested federated learning (NeFL), a framework that efficiently divides deep neural networks into submodels using both depthwise and widthwise scaling. NeFL achieves performance gain, especially for the worst-case submodel compared to baseline approaches.
  arXiv  Detail & Related papers  (2023-08-15T13:29:14Z)
• torchgfn: A PyTorch GFlowNet library [56.071033896777784]
  torchgfn is a PyTorch library that aims to address this need. It provides users with a simple API for environments and useful abstractions for samplers and losses.
  arXiv  Detail & Related papers  (2023-05-24T00:20:59Z)
• TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments [4.075095403704456]
  We introduce TorchFL, a performant library for bootstrapping federated learning experiments. TorchFL is built on a bottom-up design using PyTorch and Lightning. Being built on a bottom-up design using PyTorch and Lightning, TorchFL provides ready-to-use abstractions for models, datasets, and FL algorithms.
  arXiv  Detail & Related papers  (2022-11-01T20:31:55Z)
• NVIDIA FLARE: Federated Learning from Simulation to Real-World [11.490933081543787]
  We created NVIDIA FLARE as an open-source development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches.
  arXiv  Detail & Related papers  (2022-10-24T14:30:50Z)
• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings [51.09574369310246]
  Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models. We propose a novel cross-silo dataset suite focused on healthcare, FLamby, to bridge the gap between theory and practice of cross-silo FL. Our flexible and modular suite allows researchers to easily download datasets, reproduce results and re-use the different components for their research.
  arXiv  Detail & Related papers  (2022-10-10T12:17:30Z)
• FL_PyTorch: optimization research simulator for federated learning [1.6114012813668934]
  Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared machine learning model. FL_PyTorch is a suite of open-source software written in python that builds on top of one the most popular research Deep Learning (DL) framework PyTorch.
  arXiv  Detail & Related papers  (2022-02-07T12:18:28Z)
• Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models [65.51757376525798]
  Latte is a Python library for evaluation of latent-based generative models. Latte is compatible with both PyTorch and/Keras, and provides both functional and modular APIs.
  arXiv  Detail & Related papers  (2021-12-20T16:00:28Z)
• Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning [83.02597612195966]
  solo-learn is a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs.
  arXiv  Detail & Related papers  (2021-08-03T22:19:55Z)
• FedML: A Research Library and Benchmark for Federated Machine Learning [55.09054608875831]
  Federated learning (FL) is a rapidly growing research field in machine learning. Existing FL libraries cannot adequately support diverse algorithmic development. We introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison.
  arXiv  Detail & Related papers  (2020-07-27T13:02:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.