Research References
Federated Learning References by Google
Research
Research
- [Federated Averaging] Communication-Efficient Learning of Deep Networks from Decentralized Data
- [System design] Towards Federated Learning at Scale: System Design
- [Differential Privacy] Learning Differentially Private Recurrent Language Models
- [Differential Privacy] A General Approach to Adding Differential Privacy to Iterative Training Procedures
- [Differential Privacy] Federated Heavy Hitters Discovery with Differential Privacy
- [Efficiency] Federated Learning: Strategies for Improving Communication Efficiency
- [Efficiency] Distributed Mean Estimation with Limited Communication
- [Efficiency] cpSGD: Communication-efficient and differentially-private distributed SGD
- [Efficiency] Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
- [Learning theory] Agnostic Federated Learning
- [Optimization] Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
- [Optimization] Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- [Security] Practical Secure Aggregation for Privacy Preserving Machine Learning
Production use for language modelling
Production use for language modelling
- Federated Learning for Mobile Keyboard Prediction
- Applied Federated Learning: Improving Google Keyboard Query Suggestions
- Federated Learning Of Out-Of-Vocabulary Words
TensorFlow Federated
TensorFlow Federated
An open-source framework for machine learning and other computations on decentralized data.