timmers / awesome-federated-learning
A curated list of resources dedicated to federated learning.
☆104Updated 3 years ago
Alternatives and similar repositories for awesome-federated-learning:
Users that are interested in awesome-federated-learning are comparing it to the libraries listed below
- Federated Multi-Task Learning☆129Updated 6 years ago
- A set of tutorials to implement the Federated Averaging algorithm on TensorFlow.☆182Updated 3 years ago
- Privacy Preserving Vertical Federated Learning☆217Updated last year
- A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.☆471Updated last year
- ☆167Updated 7 years ago
- Bayesian Nonparametric Federated Learning of Neural Networks☆144Updated 5 years ago
- tf implementation of federated learning☆41Updated 5 years ago
- Code for Federated Learning with Matched Averaging, ICLR 2020.☆335Updated 3 years ago
- Related material on Federated Learning☆26Updated 4 years ago
- vertical federated learning paper lists☆75Updated 4 years ago
- Salvaging Federated Learning by Local Adaptation☆56Updated 7 months ago
- Differential Privacy Preservation in Deep Learning under Model Attacks☆133Updated 3 years ago
- A Simulator for Privacy Preserving Federated Learning☆93Updated 4 years ago
- Simulate a federated setting and run differentially private federated learning.☆369Updated 2 weeks ago
- Fair Resource Allocation in Federated Learning (ICLR '20)☆247Updated last year
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆147Updated 2 years ago
- [NeurIPS 2019 FL workshop] Federated Learning with Local and Global Representations☆232Updated 7 months ago
- ☆93Updated 3 years ago
- ☆111Updated 4 years ago
- ☆73Updated last year
- Federated Learning mini-framework with Keras☆35Updated 5 years ago
- resources about federated learning and privacy in machine learning☆531Updated 8 months ago
- A Benchmark of Real-world Image Dataset for Federated Learning☆42Updated 5 years ago
- This work combines differential privacy and multi-party computation protocol to achieve distributed machine learning.