https://github.com/zib-iol/feddistill
Code to reproduce the experiments of the ICLR25 paper "On the Byzantine-Resilience of Distillation-Based Federated Learning"
https://github.com/zib-iol/feddistill
byzantine deep-learning federated-learning knowledge-distillation neural-networks pytorch
Last synced: about 1 month ago
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Code to reproduce the experiments of the ICLR25 paper "On the Byzantine-Resilience of Distillation-Based Federated Learning"
- Host: GitHub
- URL: https://github.com/zib-iol/feddistill
- Owner: ZIB-IOL
- Created: 2024-02-19T15:56:16.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-15T20:49:54.000Z (2 months ago)
- Last Synced: 2025-03-28T13:37:16.772Z (about 2 months ago)
- Topics: byzantine, deep-learning, federated-learning, knowledge-distillation, neural-networks, pytorch
- Language: Python
- Homepage: https://arxiv.org/abs/2402.12265
- Size: 93.8 KB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Citation: citation.bib
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README
## [ICLR25] On the Byzantine-Resilience of Distillation-Based Federated Learning
*Authors: [Christophe Roux](http://christopheroux.de/), [Max Zimmer](https://maxzimmer.org/), [Sebastian Pokutta](http://www.pokutta.com/)*
This repository contains the code to reproduce the experiments from the ICLR25 paper ["On the Byzantine-Resilience of Distillation-Based Federated Learning"](https://arxiv.org/abs/2402.12265).
The code is based on [PyTorch 1.9](https://pytorch.org/) and the experiment-tracking platform [Weights & Biases](https://wandb.ai).
### Structure and Usage
#### Structure
Experiments are started from the following file:
- [`main.py`](main.py): Starts experiments using the dictionary format of Weights & Biases.
The rest of the project is structured as follows:
- [`byzantine`](byzantine): Contains the attacks and defenses used in the paper.
- [`runners`](runners): Contains classes to control the training and collection of metrics.
- [`models`](models): Contains all model architectures used.
- [`utilities.py`](utilities.py): Contains useful auxiliary functions and classes.
- [`config.py`](config.py): Configuration for the datasets used in the experiments.
- [`public_config.py`](public_config.py): Contains the configuration for the public datasets.
- [`metrics.py`](metrics.py): Contains the metrics used in the experiments.
- [`strategies.py`](strategies.py): Contains the different strategies used, such as FedAVG and FedDistill.
#### Usage
Define the parameters in the [`main.py`](main.py) defaults-dictionary and run it with the --debug flag. Or, configure a sweep in Weights & Biases and run it from there (without the flag).
### Citation
In case you find the paper or the implementation useful for your own research, please consider citing:
```
@inproceedings{roux2025on,
title={On the Byzantine-Resilience of Distillation-Based Federated Learning},
author={Christophe Roux and Max Zimmer and Sebastian Pokutta},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=of6EuHT7de}
}
```