# attacking_federate_learning

**Repository Path**: frontxiang/attacking_federate_learning

## Basic Information

- **Project Name**: attacking_federate_learning
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-04
- **Last Updated**: 2021-10-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# Federate Learning 攻防复现篇

更新日期截止2020年5月22日，项目定期维护和更新，维护各种SOTA的Federated Learning的攻防模型。(更新中。。）

## 论文 (Defend)


1. (**Krum**): Machine Learning with Adversaries:
Byzantine Tolerant Gradient Descent【NIPS 2017】
2. (**trimmed_mean**): D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett. Byzantine-robust distributed learning:
Towards optimal statistical rates. In Proceedings of the International Conference on Machine Learning (ICML), 2018.
3. (**bulyan**): E. M. El Mhamdi, R. Guerraoui, and S. Rouault. The hidden vulnerability of distributed learning in Byzantium. In Proceedings of the 35th International Conference on Machine Learning (ICML), pages 3521–3530, 2018.

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## 论文 (Attack)

4. A Little Is Enough: Circumventing Defenses For Distributed Learning【NIPS 2019】




代码运行
```
mkdir logs
python main.py

```