michaelTJC96 / Label_Flipping_AttackLinks
The project aims to evaluate the vulnerability of Federated Learning systems to targeted data poisoning attack known as Label Flipping Attack. The project studies the scenario that a malicious participant can only manipulate the raw training data on their device. Hence, non-expert malicious participants can achieve poisoning without knowing the …
☆19Updated 3 years ago
Alternatives and similar repositories for Label_Flipping_Attack
Users that are interested in Label_Flipping_Attack are comparing it to the libraries listed below
Sorting:
- FedAvg code with privacy protection function, the application of Paillier homomorphic encryption algorithm and differential privacy, diff…☆122Updated 9 months ago
- Code for Data Poisoning Attacks Against Federated Learning Systems☆195Updated 4 years ago
- nips23-Dynamic Personalized Federated Learning with Adaptive Differential Privacy☆78Updated 10 months ago
- Code for NDSS 2021 Paper "Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses Against Federated Learning"☆147Updated 2 years ago
- Implementation of dp-based federated learning framework using PyTorch☆301Updated 2 years ago
- Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shak…☆392Updated 8 months ago
- PyTorch implementation of Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance☆33Updated 9 months ago
- ☆14Updated 2 years ago
- The official code of KDD22 paper "FLDetecotor: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clien…☆84Updated 2 years ago
- A sybil-resilient distributed learning protocol.☆105Updated last year
- An implementation of Deep Learning with Differential Privacy☆25Updated 2 years ago
- FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning☆20Updated 2 years ago
- ☆39Updated last year
- Paper notes and code for differentially private machine learning☆357Updated 7 months ago
- ☆16Updated last year
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆33Updated 4 years ago
- ☆35Updated 2 years ago
- FL-Defender: Combating Targeted Attacks in Federated Learning☆1Updated 2 years ago
- An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawit…☆88Updated 5 years ago
- reproduce the FLTrust model based on the paper "FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping"☆30Updated 2 years ago
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆45Updated 2 years ago
- IEEE TIFS'20: VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning☆24Updated 2 years ago
- ⚔️ Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning☆143Updated 5 months ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆22Updated 4 years ago
- Differentially Private Federated Learning on Heterogeneous Data☆66Updated 3 years ago
- An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch☆129Updated 2 years ago
- Official implementation of "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective"…☆42Updated 3 years ago
- Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in …☆35Updated last year
- Official Implementation of "Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning"☆10Updated 5 months ago
- This repository contains the official implementation for the manuscript: Make Landscape Flatter in Differentially Private Federated Lear…☆50Updated last year