| Year | Title | Venue | Code |
|---|---|---|---|
| 2021 | Advances and Open Problems in Federated Learning | FTML | Link |
| 2019 | Federated Machine Learning: Concept and Applications | TIST | Link |
| Year | Title | Venue | Code |
|---|---|---|---|
| 2021 | Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data | The Web | Link |
| 2021 | Hierarchical Federated Learning through LAN-WAN Orchestration | The Web | Link |
| 2020 | Think Locally, Act Globally: Federated Learning with Local and Global Representations | NeurIPS workshop | Link |
| 2020 | Personalized Federated Learning: A Meta-Learning Approach | NeurIPS | Link |
| 2017 | Communication-Efficient Learning of Deep Networks from Decentralized Data | AISTATS | Link |
| Year | Title | Venue | Code |
|---|---|---|---|
| 2021 | DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce [Video] | The Web | Link |
| 2021 | FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation | arxiv | Link |
| 2020 | Robust Federated Recommendation System | arxiv | Link |
| 2020 | FEDERATED MULTI-VIEW MATRIX FACTORIZATION FOR PERSONALIZED RECOMMENDATIONS | arxiv | Link |
| 2020 | Secure Federated Matrix Factorization | Int. Sys. | CODE |
| 2020 | Privacy-Preserving News Recommendation Model Learning | EMNLP-Findings | CODE |
| 2019 | FEDERATED COLLABORATIVE FILTERING FOR PRIVACY-PRESERVING PERSONALIZED RECOMMENDATION SYSTEM | arxiv | Link |
| 2019 | Federating Recommendations Using Differentially Private Prototypes | arxiv | Link |
- https://github.com/poga/awesome-federated-learning
- https://github.com/tensorflow/federated
- https://github.com/AshwinRJ/Federated-Learning-PyTorch
- Flower https://flower.dev/
- PySyft https://github.com/OpenMined/PySyft
- Tensorflow Federated https://www.tensorflow.org/federated
- CrypTen https://github.com/facebookresearch/CrypTen
- FATE https://fate.fedai.org/
- DVC https://dvc.org/
- LEAF https://leaf.cmu.edu/
- Federated iNaturalist/Landmarkds https://github.com/google-research/google-research/tree/master/federated_vision_datasets
- FedML: A Research Library and Benchmark for Federated Machine Learning https://github.com/FedML-AI/FedML
- XayNet: Open source framework for federated learning in Rust https://xaynet.webflow.io/
MIT CSAIL/Harvard Medical/Tsinghua University’s Academy of Arts and Design
- https://arxiv.org/ftp/arxiv/papers/1903/1903.09296.pdf
- https://venturebeat.com/2019/03/25/federated-learning-technique-predicts-hospital-stay-and-patient-mortality/
Microsoft research/University of Chinese Academy of Sciences, Beijing, China
Boston University/Massachusetts General Hospital
- https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
- https://www.statnews.com/2019/09/10/google-mayo-clinic-partnership-patient-data/
Tencent WeBank
Nvidia/King’s College London, American College of Radiology, MGH and BWH Center for Clinical Data Science, and UCLA Health... etc