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.
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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
- 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?
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- Hyperparameter Optimization
- Federated Learning
- University of Oxford
- University of Cambridge
- Nvidia Jetson
- Tensorflow Lite
- Tensorflow Federated
- Flower Summit
- CNN == Convolutional Neural Network
- NumPy NDArray
- AWS Device Farm
- Ray Framework