https://github.com/ihooni/XOR-Mixup
TensorFlow experiment codes of XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning (FL' ICML 2020)
https://github.com/ihooni/XOR-Mixup
Last synced: about 2 months ago
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TensorFlow experiment codes of XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning (FL' ICML 2020)
- Host: GitHub
- URL: https://github.com/ihooni/XOR-Mixup
- Owner: ihooni
- License: mit
- Created: 2020-08-10T07:38:52.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-08-10T08:16:10.000Z (about 5 years ago)
- Last Synced: 2024-12-16T02:34:28.240Z (10 months ago)
- Language: Python
- Homepage:
- Size: 59.6 KB
- Stars: 12
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## XOR Mixup — TensorFlow experiment codes (FL' ICML 2020)
### : Privacy-Preserving Data Augmentation for One-Shot Federated Learning
### [Paper](https://arxiv.org/abs/2006.05148) | [Presentation](https://github.com/ihooni/XOR-Mixup/files/5049557/FL_ICML20_poster_session-final.pdf)
> **XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning**
> MyungJae Shin (SNUH), Chihoon Hwang (CAU), Joongheon Kim (Korea), Jihong Park(Deakin), Mehdi Bennis(Oulu), Seong-Lyun Kim(Yonsei)
>
> **Abstract** *User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and identically distributed (non-IID) data problem, in this work we develop a privacy-preserving XOR based mixup data augmentation technique, coined XorMixup, and thereby propose a novel one-shot FL framework, termed XorMixFL. The core idea is to collect other devices' encoded data samples that are decoded only using each device's own data samples. The decoding provides synthetic-but-realistic samples until inducing an IID dataset, used for model training. Both encoding and decoding procedures follow the bit-wise XOR operations that intentionally distort raw samples, thereby preserving data privacy. Simulation results corroborate that XorMixFL achieves up to 17.6% higher accuracy than Vanilla FL under a non-IID MNIST dataset.*
## Citation
```
@article{shin2020xor,
title={XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning},
author={Shin, MyungJae and Hwang, Chihoon and Kim, Joongheon and Park, Jihong and Bennis, Mehdi and Kim, Seong-Lyun},
journal={arXiv preprint arXiv:2006.05148},
year={2020}
}
```
## Author
[MyungJae Shin](https://github.com/170928), Chihoon Hwang