Found by comparing what the projects do, not just their names.
Federated learning defense system
A backdoor defense system for federated learning, designed to protect against data poisoning attacks by isolating subspace training and aggregating models with robust consensus fusion.
Privacy protector
An open-source implementation of a federated learning framework to protect data privacy in embodied agent learning for Vision-and-Language Navigation.
Federated Learning Defense
An implementation of a defense against model inversion attacks in federated learning
Federated attack generator
A tool for demonstrating and analyzing attacks on federated learning systems by introducing backdoors into distributed machine learning models.
Backdoor attacks
This project provides an implementation of backdoor attacks in federated learning frameworks using Python and PyTorch.
Federated Learning Attack
An implementation of a federated learning attack method known as Neurotoxin, which introduces backdoors into machine learning models during the training process.
Federated Breach Analysis
A PyTorch framework for analyzing vulnerabilities in federated learning models and predicting data breaches
Federated Learning Simulator
Simulates a federated learning setting to preserve individual data privacy
Federated Defense Library
An implementation of a game-theoretic defense against backdoor attacks in federated learning.
Federated Learning Attack
A framework for attacking federated learning systems with adaptive backdoor attacks
Federated Learning Framework
This project presents a framework for robust federated learning against backdoor attacks.
Gradient Attack Method
Researchers develop an attack method to measure the effectiveness of federated learning privacy defenses by generating leakage in gradients
Attack defense
A defense mechanism against model poisoning attacks in federated learning
Federated learning method
A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness
Federated Learning vulnerability research
Researchers investigate vulnerabilities in Federated Learning systems by introducing new backdoor attacks and exploring methods to defend against them.