ByzFL

ByzFL

Python library for Byzantine-resilient federated learning, compatible with PyTorch and NumPy.

ByzFL implements state-of-the-art robust aggregators (Trimmed Mean, Geometric Median, Krum, Centered Clipping, etc.) and pre-aggregators (NNM, Bucketing, ARC) as well as Byzantine attack strategies (IPM, ALIE, Sign Flipping, Mimic, …) fully compatible with PyTorch tensors and NumPy arrays. It also ships a complete federated learning simulation pipeline for systematic benchmarking under adversarial conditions.

Byzantine ResilienceDistributed LearningFederated LearningMachine Learning
Key facts
Maturity
Support
C4DT
Inactive
Lab
Active
  • Technical
  • Research papers

Distributed Computing Lab

Distributed Computing Lab
Rachid Guerraoui

Prof. Rachid Guerraoui

The Distributed Computing Lab focuses currently on Scalable Implementations of Cryptocurrencies, Byzantine fault tolerance and privacy in distributed machine learning, distributed algorithms making use of RDMA and NVRAM.

This page was last edited on 2026-03-19.