Federated learning in production (part 1)
2025-05-30 · 45 min · episode 314 · 6 entities
Asserted relationships
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0.55
evidence rules-v5
Feed author/publisher: Daniel Whitenack and Chris Benson
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evidence rules-v5
Feed category: Technology
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0.40
evidence rules-v5
Feed author/publisher: Practical AI LLC
Entities found in this episode
companys 4
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evidence rules-v5
Feed author/publisher: Practical AI LLC
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evidence rules-v5
Feed category: Technology
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evidence rules-v5
Feed category: Technology
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0.40
evidence rules-v5
Feed author/publisher: Practical AI LLC
persons 2
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evidence rules-v5
Feed author/publisher: Daniel Whitenack and Chris Benson
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0.55
evidence rules-v5
Feed author/publisher: Daniel Whitenack and Chris Benson
Episode description as stored
In this first of a two part series of episodes on federated learning, we dive into the evolving world of federated learning and distributed AI frameworks with Patrick Foley from Intel. We explore how frameworks like OpenFL and Flower are enabling secure, collaborative model training across silos, especially in sensitive fields like healthcare. The conversation touches on real-world use cases, the challenges of distributed ML/AI experiments, and why privacy-preserving techniques may become essential for deploying AI to production.
Featuring:
Patrick Foley – LinkedIn
Chris Benson – Website , GitHub , LinkedIn , X
Daniel Whitenack – Website , GitHub , X
Links:
Intel
OpenFL
Sponsors:
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