https://github.com/xtra-computing/privml
https://github.com/xtra-computing/privml
Last synced: 18 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/xtra-computing/privml
- Owner: Xtra-Computing
- Created: 2020-01-21T07:42:32.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-01-08T03:29:00.000Z (about 5 years ago)
- Last Synced: 2025-05-21T20:46:44.671Z (9 months ago)
- Size: 27.3 MB
- Stars: 19
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Private Machine Learning
## Table of Contents
* [Overview](#overview)
* [Project Descriptions](#project-descriptions)
* [Publications](#publications)
## Overview
This repo summarizes the private machine learning work of Xtra group. Currently we work mainly on two areas: federated learning and differential privacy. Federated learning enables the collaborative learning of multiple parties without exchanging the local data.
## Project Descriptions
We have worked/are working on the following projects.
(1) [Federated Learning Survey](#FL_survey): We conducted a survey on federated learning systems.
(2) [Federated Gradient Boosting Decision Trees](#SimFL): We designed a novel federated learning framework for gradient boosting decision trees.
(3) [Differentially Private Gradient Boosting Decision Trees](#DPBoost): We designed a differentially private gradient boosting decision tree training algorithm.
(4) [Federated Learning Benchmarks](#OARF): We designed a benchmark for evaluating the components in different FL systems.
## Publications
* [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://qinbinli.com/files/FLSurvey.pdf)
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Bingsheng He
arXiv preprint
* We conducted a comprehensive analysis against existing federated learning systems from different aspects (see [details](FL_survey)).
* [Practical Federated Gradient Boosting Decision Trees](https://arxiv.org/abs/1911.04206)
Qinbin Li, Zeyi Wen, Bingsheng He
Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI 2020.
* We proposed a novel federated learning framework for gradient boosting decision trees by exploiting similarity (see [details](SimFL)).
* [Privacy-Preserving Gradient Boosting Decision Trees](https://arxiv.org/abs/1911.04209)
Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He
Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI 2020.
* We designed a new differentially private gradient boosting decision trees training algorithm (see [details](DPBoost)).
* [The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems](https://arxiv.org/abs/2006.07856)
Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He
arXiv preprint.
* We designed a benchmark for evaluating the components in different FL systems (see [details](OARF), [code](https://github.com/Xtra-computing/OARF)).