TUM-AIMED / hyfed
The HyFed framework provides an easy-to-use API to develop federated, privacy-preserving machine learning algorithms.
☆16Updated 2 years ago
Related projects: ⓘ
- ☆33Updated last year
- Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology …☆20Updated 9 months ago
- Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…☆41Updated 2 years ago
- Simplicial-FL to manage client device heterogeneity in Federated Learning☆21Updated last year
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- ☆20Updated 3 years ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆24Updated last month
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆31Updated 3 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆52Updated last year
- A Simulator for Privacy Preserving Federated Learning☆93Updated 3 years ago
- PyFed generic framework of benchmark for Federated Learning☆11Updated 2 years ago
- code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"☆31Updated 2 years ago
- ☆13Updated last year
- An Efficient Learning Framework For Federated XGBoostUsing Secret Sharing And Distributed Optimization☆26Updated 2 years ago
- Code for the CCS'22 paper "Federated Boosted Decision Trees with Differential Privacy"☆42Updated 11 months ago
- Python package to create adversarial agents for membership inference attacks againts machine learning models☆46Updated 5 years ago
- Federated Learning and Membership Inference Attacks experiments on CIFAR10☆18Updated 4 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆44Updated 5 years ago
- ☆35Updated 4 months ago
- [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…☆37Updated 2 years ago
- Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradi…☆26Updated 3 years ago
- ☆39Updated last year
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆29Updated 2 years ago
- An implementation of pneumonia medical X-ray image classification problem using Federated Learning in PySyft.☆13Updated 5 years ago
- Integration of SplitNN for vertically partitioned data with OpenMined's PySyft☆26Updated 4 years ago
- This is an implementation for paper "A Hybrid Approach to Privacy Preserving Federated Learning" (https://arxiv.org/pdf/1812.03224.pdf)☆20Updated 4 years ago
- Salvaging Federated Learning by Local Adaptation☆55Updated last month
- Federated gradient boosted decision tree learning☆68Updated last year
- ☆21Updated last year
- A Federated Learning implementation to diagnose 2 acute inflammations of bladder.. This medical dataset truly needs privacy! Because we c…☆35Updated 4 years ago