jgshu

aaai_materials

aby3

A Three Party MPC framework for Machine learning and Databases

academic-homepage

My academic homepage at USTC

Attacks-and-Defenses-in-Federated-Learning

CFMTL

CIDS

Federated-Learning-PyTorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

flguard_eval

Personal evaluations of the FLGuard algorithm proposed by Nguyen et al.

MTurkTrackerData

pMatch

jgshu/Federated-Learning-PyTorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

jgshu/aaai_materials

jgshu/AdvBox

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

jgshu/adversarial-robustness-toolbox

Python library for adversarial machine learning (evasion, extraction, poisoning, verification, certification) with attacks and defences for neural networks, logistic regression, decision trees, SVM, gradient boosted trees, Gaussian processes and more with multiple framework support

jgshu/Awesome-Federated-Learning

A collection of research papers categorized into broad topics in federated learning.

jgshu/awesome-Federated-Learning-1

federated-learning

jgshu/awesome-nas-papers

Awesome Neural Architecture Search Papers

jgshu/backdoor-learning-resources

A curated list of backdoor learning resources

jgshu/backdoor_federated_learning

Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)

jgshu/BDPA

jgshu/CLDP

Secure and utility-aware data collection with condensed local differential privacy

jgshu/clustered-federated-learning

Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

jgshu/DBA

DBA: Distributed Backdoor Attacks against Federated Learning

jgshu/Deep-Leakage-from-Gradients

paper code

jgshu/dlg

[NeurIPS 2019] Deep Leakage From Gradients

jgshu/falcon-public

Implementation of protocols in Falcon

jgshu/federated-learning-secure-aggregation

A simple Python implementation of a secure aggregation protocole for federated learning.

jgshu/FedMA

Code for Federated Learning with Matched Averaging, ICLR 2020.

jgshu/FedNAS

FedNAS: Federated Deep Learning via Neural Architecture Search

jgshu/galgebra

Symbolic Geometric Algebra/Calculus package for SymPy :crystal_ball:

jgshu/ifca

Codebase for An Efficient Framework for Clustered Federated Learning.

jgshu/Improved-Deep-Leakage-from-Gradients

The code for "Improved Deep Leakage from Gradients" (iDLG).

jgshu/Labs-Federated-Learning

Accenture Labs Federated Learning

jgshu/leaf

Leaf: A Benchmark for Federated Settings

jgshu/ModelPoisoning

Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470

jgshu/multi-center-fed-learning

fully ready experiments

jgshu/PySyft

A library for encrypted, privacy preserving machine learning

jgshu/secure-aggregation

Secure aggregation for federated learning using enclaves

jgshu/textflint

Text Robustness Evaluation Platform

jgshu/trojai-literature