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cover of episode Federated learning in production (part 2)

Federated learning in production (part 2)

2025/6/4
logo of podcast Practical AI: Machine Learning, Data Science, LLM

Practical AI: Machine Learning, Data Science, LLM
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C
Chong Shen
C
Chris Benson
D
Daniel Whitenack
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Chong Shen: 我拥有计算物理的背景，并在并行计算和机器学习方面有研究经验。在从学术界转向工业界时，我对分布式学习产生了兴趣。我看到了许多项目对采用联邦学习或分布式学习方法来解决特定问题感兴趣。我加入Flower Labs是因为我热衷于开发一个具有宽松许可的开源框架，并成为该框架的核心贡献者。

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Many companies are turning to federated learning because they can't move their data due to privacy, geopolitical issues, or massive data volumes. Federated learning offers a solution by training models at the data source and aggregating model weights, avoiding the need to move sensitive data.
  • Data privacy concerns are a primary driver for adopting federated learning.
  • Immense data volumes make centralized training impractical.
  • Federated learning solves the problem by training models locally and aggregating results centrally.

Shownotes Transcript

Chong Shen from Flower Labs joins us to discuss what it really takes to build production-ready federated learning systems that work across data silos. We talk about the Flower framework and it's architecture (supernodes, superlinks, etc.), and what makes it both "friendly" and ready for real enterprise environments. We also  explore how the generative Generative AI boom is reshaping Flower’s roadmap.

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