zhaohuali / E2EGILinks
End-to-End Gradient Inversion (Gradient Leakage in Federated Learning) 【https://ieeexplore.ieee.org/document/9878027】
☆10Updated 2 years ago
Alternatives and similar repositories for E2EGI
Users that are interested in E2EGI are comparing it to the libraries listed below
Sorting:
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆57Updated 2 years ago
- Differentially Private Federated Learning: A Client Level Perspective☆12Updated 6 years ago
- Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.☆60Updated 6 years ago
- Federated Learning and Membership Inference Attacks experiments on CIFAR10☆22Updated 5 years ago
- ☆35Updated 2 years ago
- A summay of existing works on vertical federated/split learning☆15Updated 3 years ago
- Code & supplementary material of the paper Label Inference Attacks Against Federated Learning on Usenix Security 2022.☆83Updated 2 years ago
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆68Updated 4 years ago
- Membership Inference Attack on Federated Learning☆12Updated 3 years ago
- Code for USENIX Security 2023 Paper "Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks"☆20Updated last year
- Repository that contains the code for the paper titled, 'Unifying Distillation with Personalization in Federated Learning'.☆12Updated 4 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆33Updated 4 years ago
- [IJCNN 2021] FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning☆11Updated 3 years ago
- Source code for MLSys 2022 submission "LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning"☆23Updated 3 years ago
- reveal the vulnerabilities of SplitNN☆31Updated 3 years ago
- Adversarial attacks and defenses against federated learning.☆18Updated 2 years ago
- Eluding Secure Aggregation in Federated Learning via Model Inconsistency☆12Updated 2 years ago
- Differentially Private Federated Learning on Heterogeneous Data☆66Updated 3 years ago
- ☆30Updated 5 years ago
- ☆9Updated 3 years ago
- Chain-PPFL: A Privacy-Preserving Federated Learning Framework based on Chained SMC☆35Updated 5 years ago
- ☆37Updated 3 years ago
- ☆35Updated 3 years ago
- ☆55Updated 2 years ago
- ☆35Updated 4 years ago
- ☆18Updated 4 years ago
- Code for paper "Interpret Federated Learning with Shapley Values"☆39Updated 6 years ago
- Code implementation of the paper "Federated Unlearning: How to Efficiently Erase a Client in FL?" published at UpML (part of ICML 2022)☆39Updated 2 months ago
- ☆29Updated 2 years ago
- 基于《A Little Is Enough: Circumventing Defenses For Distributed Learning》的联邦学习攻击模型☆63Updated 5 years ago