Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data

Re:Launch HN: Flower (YC W23) – Train AI models on distribut... by jorgeili on Wednesday March 22, 2023 @11:57AM (#35263060)

What about MPC + DP? Are you planning to integrate any SMPC algorithms on flower or do you find any limitations for not doing so.

I'm trying to apply federated learning to the medical domain too and I'm trying to define the best "stack" that guarantees privacy and compliance with regulations like the GDPR

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Re:Launch HN: Flower (YC W23) – Train AI models on distribut... by williamtrask on Wednesday March 22, 2023 @01:42PM (#35264508)

I can’t speak for Flower’s core dev roadmap, but PySyft is in the process of integrating Flower and some Secure Enclave options which would let you do this.

Congrats on the launch Flower team!

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Re:Launch HN: Flower (YC W23) – Train AI models on distribut... by danieljanes on Wednesday March 22, 2023 @04:53PM (#35266955)

Thanks! We're huge fans of the work that PySyft is doing, and we're very supportive of the Flower PySyft integration.

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Re:Launch HN: Flower (YC W23) – Train AI models on distribut... by danieljanes on Wednesday March 22, 2023 @01:45PM (#35264563)

Agreed that this is an interesting direction. The core Flower abstractions are "federated learning agnostic", which means that they can be used for different kinds of distributed/federated workloads, not just federated learning. We'll add examples for more approaches (like SMPC) in the future, we just don't have the bandwidth to do it immediately.

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