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Federated learning is increasingly practical for machine learning developers because of the challenges we face with model and data privacy. In this fully connected episode, Chris and Daniel dive into the topic and dissect the ideas behind federated learning, practicalities of implementing decentralized training, and current uses of the technique.
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Frameworks/ open source projects:
Example uses of Federated Learning:
- Federated Learning for Mobile Keyboard Prediction
- Your voice & audio data stays private while Google Assistant improves
- Facebook is rebuilding its ads to know a lot less about you
- Federated learning for predicting clinical outcomes in patients with COVID-19
Something missing or broken? PRs welcome!
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