Daniel Beutel
Daniel is one of the creators of Flower, the first fully agnostic federated learning framework, which is now being used at many Fortune 500 companies and most top universities worldwide. He previously held roles as Head of AI and CTO and has considerable experience in running and scaling engineering teams. Daniel is a CS PhD candidate at the University of Cambridge and has an MSc (with distinction) in Software Engineering from the University of Oxford.
Sessions
Federated learning, a transformative technique, not only overcomes data limitations and privacy challenges but also enhances the trustworthiness of machine learning. By moving computation to data sources, it ensures privacy while enabling collaborative model training on vastly more data than before. This keynote introduces federated learning, demonstrates how Python developers can implement it in under 20 lines of code using the Flower framework (https://flower.dev), and provides an outlook on how federated learning will shape the next generation of machine learning systems.