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

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

#21

Post by guites »

tanto wrote: post #15
guites wrote: post #12

Hey! Glad to see flower getting attention on hn. I've been working on a project for over a year that uses flower to train cv models on medical data. One aspect that we see being brought up again and again is how we can prove to our clients that no unnecessary data is being shared over the network. Do you have any tips on solving that particular problem? I.e. proving that no data apart from model weights are being tra…

Hi guites, Thank you! That is undoubtedly something relatable. We have it on the screen and plan to provide helpful material and presentations helping to convince stakeholders. If you are up for a call to share the specific challenges, we could ideate with you.

Would love to! You can grab my email on my profile. Could you ping me over there? Thanks

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

#22

Post by guites »

danieljanes wrote: post #17
guites wrote: post #12

Hey! Glad to see flower getting attention on hn. I've been working on a project for over a year that uses flower to train cv models on medical data. One aspect that we see being brought up again and again is how we can prove to our clients that no unnecessary data is being shared over the network. Do you have any tips on solving that particular problem? I.e. proving that no data apart from model weights are being tra…

Thanks, glad you like it! One approach to increase the transparency on the client side (and build trust with the organization where the Flower clien is deployed) is to integrate a review step that asks the someone to confirm the update that gets send back to the server. On top of that, you should definitely use differential privacy. To quote Andrew Trask here: "friends don't let friends use FL without DP". Other appr…

A review steps sounds like a good idea. Our implementation involves very little interaction on the client side, besides setting up the datasets etc, so maybe a way to log information sent for later inspection would help.

I'll be looking into secure aggregation as I'm not fully aware of how it works. As of now we rely on differential privacy only.

Thanks!

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

#23

Post by niclane7 »

technologia wrote: post #8
niclane7 wrote: post #5

Earlier quoted context omitted.

Yes, we have developed modular and efficient secure aggregation and differential privacy solutions that can help people dial in the amount of protection they need. We have documented an early version of the secure aggregation here: https://flower.dev/docs/secagg.html Documentation and updates on both methods will be released soon.

I hate to ask a product comparison question, but why would I use this versus other projects like PySyft.

Thanks for the question, very natural to ask. We are also fans of PySft. It offers support for a very wide range of privacy enhancing machine learning tools. But where Flower and PySft differ is in focus. Federated learning is difficult and requires many technical moving parts all working together (e.g., secure aggregation, differential privacy, scalable simulation, device deployments, integration with conventional ML frameworks etc.). All of these need to tightly integrated, and in a manner that performs federated learning efficiently. This is where Flower currently excels. It offers comprehensive, extensible and, most important, easy to use construction of federations that need these different parts together. We believe it offers the best user experience for federated learning currently out there. We hope in the future many tool suites that offer private machine learning (like PySft and others) will actually adopt Flower components so we can all work better together.

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

#24

Post by technologia »

niclane7 wrote: post #23
technologia wrote: post #8

Earlier quoted context omitted.

I hate to ask a product comparison question, but why would I use this versus other projects like PySyft.

Thanks for the question, very natural to ask. We are also fans of PySft. It offers support for a very wide range of privacy enhancing machine learning tools. But where Flower and PySft differ is in focus. Federated learning is difficult and requires many technical moving parts all working together (e.g., secure aggregation, differential privacy, scalable simulation, device deployments, integration with conventional M…

I appreciate you taking the time to break this down, I’ve spent a decent chunk of time having to roll my own stuff so when pygrid/pysyft came along it was just easier. I will say the flower components look interesting and I’ll give it a shot

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

#25

Post by juanma91p »

Great to see Flower here! We use the framework for our projects because of its modularity, scalability, and ease to use. Another important aspect of FL, on top of the already mentioned privacy preservation, is network resource utilisation. By transferring only the weights of the model, less bandwidth is required, which can reduce network congestion. This is especially important given that it is expected that by 2030, more than 50 billion devices will be connected and transferring data.

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

#26

Post by williamtrask »

jorgeili wrote: post #19
danieljanes wrote: post #17

Earlier quoted context omitted.

Thanks, glad you like it! One approach to increase the transparency on the client side (and build trust with the organization where the Flower clien is deployed) is to integrate a review step that asks the someone to confirm the update that gets send back to the server. On top of that, you should definitely use differential privacy. To quote Andrew Trask here: "friends don't let friends use FL without DP". Other appr…

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

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!

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

#27

Post by williamtrask »

niclane7 wrote: post #23
technologia wrote: post #8

Earlier quoted context omitted.

I hate to ask a product comparison question, but why would I use this versus other projects like PySyft.

Thanks for the question, very natural to ask. We are also fans of PySft. It offers support for a very wide range of privacy enhancing machine learning tools. But where Flower and PySft differ is in focus. Federated learning is difficult and requires many technical moving parts all working together (e.g., secure aggregation, differential privacy, scalable simulation, device deployments, integration with conventional M…

Can confirm that PySyft is currently in the process of integrating with Flower. Best of both worlds.

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

#28

Post by danieljanes »

jorgeili wrote: post #19
danieljanes wrote: post #17

Earlier quoted context omitted.

Thanks, glad you like it! One approach to increase the transparency on the client side (and build trust with the organization where the Flower clien is deployed) is to integrate a review step that asks the someone to confirm the update that gets send back to the server. On top of that, you should definitely use differential privacy. To quote Andrew Trask here: "friends don't let friends use FL without DP". Other appr…

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

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.

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

#29

Post by danieljanes »

elijahbenizzy wrote: post #20

Congratulations! Really excited for you! I love how you found a niche, valuable problem, built a framework, and are seeing a lot of success. A question (and I'm far from an expert so let me know if the assumptions are wrong): It seems to me that the federated users have to be coordinated around timing for this to work. Otherwise this could take weeks/lots of slack messages for a single model to train. E.G. one team i…

Thanks, we're excited too!

Flower tries to automate this as much as it can. In cases where multiple organizations are involved, the workload can run in a fully automated manner if that's fine for all organizations. If a review step is required, that can be integrated (either on the client side or on the server side) - the availability of reviewers will then become the bottleneck for end-to-end latency.

In the long run, we will evolve the permissioning system to allow workloads to be automatically executed if they fall within pre-approved boundaries, or require manual review if they don't. Pre-approved boundaries could, for example, be used to configure a particular combination of models and hyperparemter ranges that are ok to run without additional (manual) approvals.

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