Practical AI Federated learning in production (part 2)
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Jun 4, 2025 
Chong Shen, a research engineer at Flower Labs with a background in computational physics, shares insights on building production-ready federated learning systems. He discusses the user-friendly Flower framework, essential for handling sensitive data across industries. Topics include the design challenges for developers, the integration of diverse models, and how the generative AI boom is influencing future developments. The conversation sheds light on the balance between usability and production demands, especially in sectors like healthcare.
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Federated Learning Growth and Scale
- Federated learning adoption has grown about 100 times since 2021, driven by training foundational and large language models.
- The framework's architecture evolved to handle large model weights enabling new use cases.
Flower Framework Overview
- Flower is a Python-based open source framework allowing users to build federated learning apps easily.
- It emphasizes user experience with strong community support and open transparency.
Student Experience with Flower
- Students learning federated learning favored Flower because of its Pythonic design and ease of use.
- Its user-friendly approach helped newcomers rapidly prototype their federated training apps.