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.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.comLaunchDarkly – 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, XDaniel Whitenack – Website, GitHub, XShow Notes:Learning:Google Federated Learning comicFederated Learning: A Step by Step Implementation in TensorflowFrameworks/ open source projects:TensorFlow FederatedIntel Open Federated LearningPyGridFlowerExample uses of Federated Learning:Federated Learning for Mobile Keyboard PredictionYour voice & audio data stays private while Google Assistant improvesFacebook is rebuilding its ads to know a lot less about youFederated learning for predicting clinical outcomes in patients with COVID-19Upcoming Events: Register for upcoming webinars here!