Federated Learning 📱

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. Join the discussion Changelog++ members get a bonus 2 minutes at the end of this episode and zero ads. Join today! Sponsors: RudderStack – Smart customer data pipeline made for developers. RudderStack is the smart customer data pipeline. Connect your whole customer data stack. Warehouse-first, open source Segment alternative. Changelog++ – You love our content and you want to take it to the next level by showing your support. We’ll take you closer to the metal with no ads, extended episodes, outtakes, bonus content, a deep discount in our merch store (soon), and more to come. Let’s do this ! Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com LaunchDarkly – Ship fast. Rest easy. Deploy code at any time, even if a feature isn’t ready to be released to your users. Wrap code in feature flags to get the safety to test new features and infrastructure in prod without impacting the wrong end users. Featuring: Chris Benson – Website , GitHub , LinkedIn , X Daniel Whitenack – Website , GitHub , X Show Notes: Learning: Google Federated Learning comic Federated Learning: A Step by Step Implementation in Tensorflow Frameworks/ open source projects: TensorFlow Federated Intel Open Federated Learning PyGrid Flower 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!