I've been working with Flower to implement and study Federated Learning for a few years, and have just started contributing back on Slack and Github. Congrats on launching on HN!
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
#12Hey! 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 transferred to the centralized server?
Thanks a lot for the project.
edit: Just to clarify I am aware of differential privacy, I'm talking more on a "how to convince a medical institution that we are not sending its images over the network" level.
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#13I've been working with Flower to implement and study Federated Learning for a few years, and have just started contributing back on Slack and Github. Congrats on launching on HN!
Really happy to hear that and your support is much appreciated! I saw you answering many questions before we could do so :) Thank you for that. We are reaching out to all contributor. Let me know in Slack if you are up to a short call to understand better how we can support you.
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#14Hey! 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…
If you're concerned about data leakage, it's worth noting that model weights can very easily be used to reconstruct the original data that it was trained on: so it could be misleading to claim that user data isn't being shared over the network. To avoid this, you'd need to look into techniques like Secure Aggregation or local differential privacy. Flower does provide some of this, FWIW.
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#15Hey! 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.
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#16I've been working with Flower to implement and study Federated Learning for a few years, and have just started contributing back on Slack and Github. Congrats on launching on HN!
Really happy to hear that and your support is much appreciated! I saw you answering many questions before we could do so :) Thank you for that. We are reaching out to all contributor. Let me know in Slack if you are up to a short call to understand better how we can support you.
Absolutely. Happy to help!
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#17Hey! 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 approaches like Secure Aggregation can also help, depending on what kind of exposure your clients are concerned about.
My general take is that the best way to solve for transparency and trust is to tackle it on multiple layers of the stack.
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#18This 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?
Yes, this would be exciting to see. One approach wouldn't require federated learning however. If you had direct access to the data then you could build a conventionally trained large language model (i.e., collect all the data together placed in a data center). However, given the context of this discussion -- you are probably asking about if we could use Flower to train in a federated manner. I believe so. Although again, we'd probably be training a LLM which brings added complications due to its size (and other factors). Internally at Flower we have been testing methods to overcome this and are confident we can pull this off. One could imagine someone hosting a pre-trained LLM and contributing institutions acting as nodes in the network, each performing some small part of the training based on the fraction of the literature they have access to. We plan to release LLM based federated technology in the coming months.
For those that are interested: The best work currently I've seen on training very large models under federated learning, that also makes very realistic assumptions about the likely underlying participating hardware, is this: https://arxiv.org/abs/2206.11239 -- although I expect more in this direction to come soon.
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#19Hey! 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…
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
Re: Launch HN: Flower (YC W23) – Train AI models on distributed or sensitive data
#20Congratulations! 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 is having infra issues and doesn't get a job started, the other team is ready but then their lead goes on vacation, etc... In the internal-to-an-organization case this is probably fine (E.G. a hospital where the data has to be separated by patient/cohort), but if there are different teams managing the data then (a) have you seen this problem and (b) do you have tooling to fix it?