Federated Learning For All With Flower

Summary Machine learning is a tool that has typically been performed on large volumes of data in one place. As more computing happens at the edge on mobile and low power devices, the learning is being federated which brings a new set of challenges. Daniel Beutel co-created the Flower framework to make federated learning more manageable. In this episode he shares his motivations for starting the project, how you can use it for your own work, and the unique challenges and benefits that this emerging model offers. This is a great exploration of the federated learning space and a framework that makes it more approachable. Announcements Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. 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Your host as usual is Tobias Macey and today I’m interviewing Daniel Beutel about Flower, a framework for building federated learning systems Interview Introductions How did you get introduced to Python? Can you start by describing what federated learning is? What is Flower and what’s the story behind it? What are the trade-offs between federated and centralized models of machine learning? What are some of the types of use cases or workloads that federated learning is used for? Federated learning appears to be a growing area of interest. How would you characterize the current state of the ecosystem? What are the most complex or challenging aspects of federating model training? How does Flower simplify the process of distributing the model training process? Can you describe how Flower is implemented? How have the goals and/or design of Flower changed or evolved since you first began working on it? One of the design principles that you list is "understandability". What are some of the ways that that manifests in the project? It also mentions extensibility. What are the interfaces that Flower exposes for integration or extending its capabilities? For someone who has an existing project that runs in a centralized manner, what are some indicators that a federated approach would be beneficial? What is involved in translating the existing project to run in a federated fashion using Flower? What is involved in building a production ready system with Flower? How does your work at Adap inform the design and product direction for Flower? What are some of the most interesting, innovative, or unexpected ways that you have seen Flower used? What are the most interesting, unexpected, or challenging lessons that you have learned from your work on and with Flower? When is Flower the wrong choice? What do you have planned for the future of the project? Keep In Touch LinkedIn danieljanes on GitHub @daniel_janes on Twitter Picks Tobias Rummy Card Game Daniel Stand Up Paddling Closing Announcements Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at pythonpodcast.com/chat Links Flower Adap Hyperparameter Optimization Federated Learning University of Oxford University of Cambridge Nvidia Jetson PyTorch Podcast Episode Tensorflow Lite Tensorflow Federated PySyft Flower Summit Jax CNN == Convolutional Neural Network Keras gRPC MQTT NumPy NDArray AWS Device Farm Ray Framework Podcast Episode The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA