mlsanityData to 14 Oct 2026

ByzFL: Research Framework for Robust Federated Learning

arXiv 2505.24802 ↗First posted 30 May 2025cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 33 of its 47 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

33 of 47 references.

References · 33

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2018Federated Learning with Non-IID Datacited by 1,108
  6. 2020Adaptive Federated Optimizationcited by 1,046
  7. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  8. 2018LEAF: A Benchmark for Federated Settingscited by 792
  9. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  10. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
  11. 2022Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Modelcited by 559
  12. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
21 more
  1. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  2. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  3. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  4. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  5. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  6. 2020Learning from History for Byzantine Robust Optimizationcited by 137
  7. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 128
  8. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  9. 2018A generic framework for privacy preserving deep learningcited by 103
  10. 2018Generalized Byzantine-tolerant SGDcited by 95
  11. 2023Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneitycited by 65
  12. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
  13. 2020Byzantine-Resilient Non-Convex Stochastic Gradient Descentcited by 57
  14. 2023Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneitycited by 26
  15. 2023On the Privacy-Robustness-Utility Trilemma in Distributed Learningcited by 24
  16. 2018Universal Multi-Party Poisoning Attackscited by 17
  17. 2024Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updatescited by 14
  18. 2022Robust Collaborative Learning with Linear Gradient Overheadcited by 12
  19. 2024Adaptive Gradient Clipping for Robust Federated Learningcited by 11
  20. 2025Towards Trustworthy Federated Learning with Untrusted Participantscited by 8
  21. 2022Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learningcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026SecJev: Bringing Security Expertise to System One Decision Modelscited by 1
  2. 2026From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similaritycited by 0
  3. 2026Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learningcited by 0
  4. 2026Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustnesscited by 0