PatrickZH / Improved-Deep-Leakage-from-Gradients

Related projects ⓘ

Alternatives and complementary repositories for Improved-Deep-Leakage-from-Gradients

• AI-secure / DBA
  DBA: Distributed Backdoor Attacks against Federated Learning (ICLR 2020)
  ☆176Updated 3 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆71Updated 3 years ago
• dayu11 / Differentially-Private-Deep-Learning
  This repo implements several algorithms for learning with differential privacy.
  ☆102Updated last year
• vrt1shjwlkr / NDSS21-Model-Poisoning
  Code for NDSS 2021 Paper "Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses Against Federated Learning"
  ☆135Updated 2 years ago
• ebagdasa / pytorch-privacy
  simple Differential Privacy in PyTorch
  ☆48Updated 4 years ago
• JonasGeiping / invertinggradients
  Algorithms to recover input data from their gradient signal through a neural network
  ☆274Updated last year
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 5 years ago
• git-disl / DataPoisoning_FL
  Code for Data Poisoning Attacks Against Federated Learning Systems
  ☆169Updated 3 years ago
• zaixizhang / FLDetector
  The official code of KDD22 paper "FLDetecotor: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clien…
  ☆74Updated last year
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆55Updated last year
• inspire-group / ModelPoisoning
  Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
  ☆146Updated 2 years ago
• ksreenivasan / OOD_Federated_Learning
  ☆65Updated 2 years ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆56Updated last month
• diogenes0319 / FedMD_clean
  FedMD: Heterogenous Federated Learning via Model Distillation
  ☆149Updated 3 years ago
• FLAIR-THU / VFLAIR
  THU-AIR Vertical Federated Learning general, extensible and light-weight framework
  ☆84Updated 4 months ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆52Updated 5 years ago
• YMJS-Irfan / DP-FedSAM
  This repository contains the official implementation for the manuscript: Make Landscape Flatter in Differentially Private Federated Lear…
  ☆41Updated last year
• csong27 / membership-inference
  Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
  ☆187Updated 7 years ago
• jhcknzzm / Federated-Learning-Backdoor
  ICML 2022 code for "Neurotoxin: Durable Backdoors in Federated Learning" https://arxiv.org/abs/2206.10341
  ☆63Updated last year
• shaneson0 / attacking_federate_learning
  基于《A Little Is Enough: Circumventing Defenses For Distributed Learning》的联邦学习攻击模型
  ☆61Updated 4 years ago
• ebagdasa / backdoor_federated_learning
  Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)
  ☆272Updated 3 months ago
• wenzhu23333 / Federated-Learning
  An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch
  ☆115Updated last year
• ppmlguy / DP-AGD
  Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget
  ☆47Updated 6 years ago
• jgshu / Attacks-and-Defenses-in-Federated-Learning
  ☆38Updated 3 years ago
• git-disl / CPL_attack
  ☆34Updated 2 years ago
• lishenghui / blades
  Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
  ☆133Updated 3 months ago
• wanglun1996 / secure-robust-federated-learning
  ☆53Updated last year
• epfml / byzantine-robust-noniid-optimizer
  ☆15Updated 2 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆129Updated last year
• Jaskiee / GAN-Attack-against-Federated-Deep-Learning
  The reproduction of the paper Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning.
  ☆57Updated last year