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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

#31

Post by ngneer »

guites wrote: post #22
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…

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!

Cool. I saw a proposal to use TEEs for secure aggregation. OpenFL uses Gramine for that. Not sure if that provides sufficient protection, really, but worth having on the radar.

https://arxiv.org/abs/2105.06413 https://openfl.readthedocs.io/en/latest/index.html https://gramineproject.io/

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

#32

Post by yawnxyz »

Hi! As someone new to all of this — how would I interact with the trained data after it's been trained?

Is it possible to create a conversation or QA style interaction with it? I see there's examples of "pytorch" but as a someone new— I'm not sure what that means in terms of public use cases.

I guess I'm asking is "ok I use Flower to train on a bunch of stuff... then what do I do with that?"

Thanks!

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

#33

Post by danieljanes »

yawnxyz wrote: post #32

Hi! As someone new to all of this — how would I interact with the trained data after it's been trained? Is it possible to create a conversation or QA style interaction with it? I see there's examples of "pytorch" but as a someone new— I'm not sure what that means in terms of public use cases. I guess I'm asking is "ok I use Flower to train on a bunch of stuff... then what do I do with that?" Thanks!

Hi there - the data never moves if you train a model using federated learning. It stays on user devices or in organizational silos. After the training, you have the model parameters of the model on the server, without the server having ever seen a single data example.

After the training, you can deploy the model in different ways. If you want to use it on device (or in one of the organizational silos), you can send the final model parameters there and deploy it locally. Or you just deploy the model on the server behind an API. It all depends on the use case.

Hope that helps, I'm happy to provide more details.

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

#34

Post by elijahbenizzy »

danieljanes wrote: post #29
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 ru…

Awesome! Makes sense. I think the challenge is going to be coordinating with the various orchestration systems -- timeouts, etc.. Excited to see how you pull it off!

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

#36

Post by haldujai »

photochemsyn wrote: post #10

This looks very interesting. I'd like to see a model trained on the complete body of scientific research literature from the past 100 years or so, I wonder if this approach could facilitate that?

I'm not sure that this would be as useful as one might think at face value. When you stretch out the training corpus like that you're going to have more noise/inaccuracies/refuted facts then you will have correct information.

It's also unclear how useful full scientific articles are, Microsoft/PubMedBERT interestingly showed PMC abstracts was better than full text.

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

#37

Post by danieljanes »

rjtc wrote: post #35

How is your approach different than tf federated or any of the other federated libraries out there?

There are some similarities, but also some differences. Flower's take is that it wants to support the entire FL workflow from experimental research to large-scale production deployments and operation. Some other FL frameworks fall either in the "research" or "production deployment" bucket, but few have good support for both.

Flower does a lot under the hood to support these different usage scenarios: it has both a networked engine (gRPC, experimental support for REST, and the possibility to "bring your own communication stack") and a simulation engine to support both real deployment on edge devices/server and simulation of large-scale federations on single machines or compute clusters.

This is - to the best of our knowledge - one of the drivers of our large and active community. The community is very collaborative and there are many downstream projects in the ecosystem that build on top of Flower (GitHub lists 748 dependent projects: https://github.com/adap/flower/network/dependents).

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

#38

Post by sisso97 »

fedgenerativeai wrote: post #30

[dead]

I'm very familiar with FL frameworks. I chose Flower after having spent a lot of time benchmarking many of them. FedML wasn't able to carry on the easiest workloads I tried, like the baseline of the Shakespeare dataset. The simulator you have was the slowest compared to the other frameworks I tried. Your platform doesn't give developers like me what they want. Flower was the best to use since day one.

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

#39

Post by danieljanes »

williamtrask wrote: post #26
jorgeili wrote: post #19

Earlier quoted context omitted.

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!

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

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

#40

Post by danieljanes »

juanma91p wrote: post #25

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,…

Great to hear, thanks for sharing - modularity, scalability, and user friendliness are what we think a lot about :)
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