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:
- Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shak…☆408Updated last year
- Code for Data Poisoning Attacks Against Federated Learning Systems☆201Updated 4 years ago
- Implementation of dp-based federated learning framework using PyTorch☆312Updated 2 years ago
- nips23-Dynamic Personalized Federated Learning with Adaptive Differential Privacy☆84Updated last year
- Paper notes and code for differentially private machine learning☆361Updated 2 months ago
- An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch☆132Updated 2 years ago
- Code for NDSS 2021 Paper "Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses Against Federated Learning"☆147Updated 3 years ago
- FedAvg code with privacy protection function, the application of Paillier homomorphic encryption algorithm and differential privacy, diff…☆130Updated last year
- An implementation of Deep Learning with Differential Privacy☆26Updated 2 years ago
- ☆35Updated 3 years ago
- reproduce the FLTrust model based on the paper "FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping"☆32Updated 2 years ago
- PyTorch implementation of Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance☆35Updated last year
- Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in …☆36Updated last year
- FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning☆20Updated 2 years ago
- ☆20Updated 2 years ago
- IEEE TIFS'20: VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning☆25Updated 3 years ago
- Accenture Labs Federated Learning☆105Updated last year
- A sybil-resilient distributed learning protocol.☆104Updated 2 months ago
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆46Updated 2 years ago
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆67Updated 4 years ago
- ⚔️ Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning☆149Updated 8 months ago
- We provide a friendly foundational platform for beginners who intend to start Federated Learning (FL)☆17Updated 5 months ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆21Updated 4 years ago
- An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawit…☆89Updated 6 years ago
- Standard federated learning implementations in FedLab and FL benchmarks.☆153Updated last year
- Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)☆306Updated last year
- ☆40Updated last year
- Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints☆178Updated 4 years ago
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆34Updated 4 years ago
- Official Implementation of "Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning"☆10Updated 8 months ago