yiyiyi4321 / Vertical-Federated-LearningLinks
☆12Updated 5 years ago
Alternatives and similar repositories for Vertical-Federated-Learning
Users that are interested in Vertical-Federated-Learning are comparing it to the libraries listed below
Sorting:
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆151Updated 2 years ago
- THU-AIR 联邦学习隐私与安全☆13Updated 2 years ago
- code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"☆31Updated 3 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆54Updated 6 years ago
- reveal the vulnerabilities of SplitNN☆31Updated 3 years ago
- 基于《A Little Is Enough: Circumventing Defenses For Distributed Learning》的联邦学习攻击模型☆64Updated 5 years ago
- A list of papers using/about Federated Learning especially malicious client and attacks.☆12Updated 5 years ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆194Updated 7 years ago
- An implementation for the paper "A Little Is Enough: Circumventing Defenses For Distributed Learning" (NeurIPS 2019)☆26Updated 2 years ago
- A sybil-resilient distributed learning protocol.☆105Updated last year
- Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness (IJCAI'19).☆13Updated 4 years ago
- ☆37Updated 3 years ago
- Federated Learning on XGBoost☆46Updated 5 years ago
- Code for paper "Interpret Federated Learning with Shapley Values"☆39Updated 6 years ago
- The code for "Improved Deep Leakage from Gradients" (iDLG).☆154Updated 4 years ago
- ☆19Updated 3 years ago
- Differential Privacy Preservation in Deep Learning under Model Attacks☆135Updated 4 years ago
- vertical federated learning paper lists☆76Updated 4 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆58Updated 2 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆135Updated 2 years ago
- Adversarial attacks and defenses against federated learning.☆19Updated 2 years ago
- DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation☆16Updated 5 years ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆22Updated 4 years ago
- ☆45Updated last year
- Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)☆301Updated last year
- CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)☆73Updated 4 years ago
- Privacy Risks of Securing Machine Learning Models against Adversarial Examples☆44Updated 5 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 4 years ago
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆68Updated 4 years ago
- Privacy attacks on Split Learning☆42Updated 3 years ago