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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

#51

Post by niclane7 »

ngneer wrote: post #31
guites wrote: post #22

Earlier quoted context omitted.

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/

Flower has an agreement to develop interoperable components with OpenFL. This is part of the broader plan by Intel to work with a consortium of players (that includes Flower Labs) and have the output code sit with the Linux Foundation. Enabling TEE support within OpenFL for SA assessible to Flower users is precisely the type of opportunities we seek to make possible by working with Intel on this.

This is the official press release for those who are interesed: https://www.intel.com/content/www/us/en/newsroom/news/transi...

More broadly, in regards too your comment -- our current SA support does not require hardware support, which is what we targeted first, so that can be broadly adopted in many potential hosts of FL aggregation servers. It is suitable for most applications in need of privacy, although still requires certain assumptions to be met such as the number of nodes within a round, and other factors.

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

#52

Post by niclane7 »

JohnFen wrote: post #48

Isn't this still moving your data to a central repository? It's encoded in a neural net rather than in a more accessible form, but it's still being moved out of your control.

It is reasonable to think of it that way. Certainly high-level information from the data is extracted and embedded within a model, but only the information necessary for the model being trained. Whereas if the data itself was being sent, then all of the information is available. Additionally, through added protections (differential privacy being one) it is possible to engineer the federated system such that the data itself can not be reconstructed from model itself.

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

#53

Post by dang »

niclane7 wrote: post #52
JohnFen wrote: post #48

Isn't this still moving your data to a central repository? It's encoded in a neural net rather than in a more accessible form, but it's still being moved out of your control.

It is reasonable to think of it that way. Certainly high-level information from the data is extracted and embedded within a model, but only the information necessary for the model being trained. Whereas if the data itself was being sent, then all of the information is available. Additionally, through added protections (differential privacy being one) it is possible to engineer the federated system such that the data…

Can you say more about what differential privacy is and how it works, for those of us who don't know or don't remember?

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

#55

Post by fedgenerativeai »

fednongenai wrote: post #41
fedgenerativeai wrote: post #30

[dead]

As a founder of an FL startup, I strongly advise against using FedML as it is one of the worst frameworks in terms of scalability. Regrettably, the FedML team has also a poor reputation due to their toxicity and suspicious behavior. If you value the trustworthiness of your product, I suggest avoiding FedML at all costs.

OMG, hard to believe that this is from Flower founder's words. I am just a federated learning user and told the truth to the audience here. I have followed the FL community for more than 2 years, and I clearly feel that Flower has been copying and following FedML's product plan without any originality at all. These nonfactual attacks further makes me distrust on Flower team. You are ruining the reputation of YC. You need to gain the trust of users like FedML team, not attack their reputation. Please stop doing so.

FedML is definitely much simpler and more powerful in both research and production. In terms of scalability, I like the benchmarking results in this paper (https://arxiv.org/abs/2303.01778); it shows that FedML is much more scalable. I also tried it myself, it's faster and more scalable. In terms of "research to production", I don't see Flower supporting any MLOps functionalities. Is Flower trying to copy FedML again in this direction?

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

#56

Post by onethought »

cpmpcpmp wrote: post #14
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…

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.

This doesn’t sound right, if they don’t know the structure of the NN how can the reconstruct from the weights alone? (Perhaps the structure is communicated within the weights?)

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

#57

Post by aix1 »

onethought wrote: post #56
cpmpcpmp wrote: post #14

Earlier quoted context omitted.

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.

This doesn’t sound right, if they don’t know the structure of the NN how can the reconstruct from the weights alone? (Perhaps the structure is communicated within the weights?)

Every agent training the model on their proprietary data has to have access to the model form in some way (otherwise how would they train it?)

For this reason, one must assume that the model form is known to the adversary.

With this, the question becomes: is it possible to reconstruct training data from a trained model? We already know that, at least for some image models, the answer to that question is "yes": https://arxiv.org/pdf/2301.13188.pdf

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

#58

Post by fednongenai »

fedgenerativeai wrote: post #55
fednongenai wrote: post #41

Earlier quoted context omitted.

As a founder of an FL startup, I strongly advise against using FedML as it is one of the worst frameworks in terms of scalability. Regrettably, the FedML team has also a poor reputation due to their toxicity and suspicious behavior. If you value the trustworthiness of your product, I suggest avoiding FedML at all costs.

OMG, hard to believe that this is from Flower founder's words. I am just a federated learning user and told the truth to the audience here. I have followed the FL community for more than 2 years, and I clearly feel that Flower has been copying and following FedML's product plan without any originality at all. These nonfactual attacks further makes me distrust on Flower team. You are ruining the reputation of YC. You…

*just a federated learning user* flashing a two-week old academic junk from the FedML team. Sus af. No f'ing way I am using a privacy product from the glorious land that gave us Tiktok and mass government surveillance.

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

#59

Post by aix1 »

dang wrote: post #53
niclane7 wrote: post #52

Earlier quoted context omitted.

It is reasonable to think of it that way. Certainly high-level information from the data is extracted and embedded within a model, but only the information necessary for the model being trained. Whereas if the data itself was being sent, then all of the information is available. Additionally, through added protections (differential privacy being one) it is possible to engineer the federated system such that the data…

Can you say more about what differential privacy is and how it works, for those of us who don't know or don't remember?

I too would be interested in understanding this better.

Let's say we're building a medical segmentation model, which takes a patient image and outlines a tumour (or some other feature that's unique to them). I am not sure this matters here, but let's say the model is a basic 2D U-net. Image pixels in, binary pixel labels out (cancer/non-cancer).

At a high level, how would a differentially-private setup work for training such a model across multiple institutions without pooling their patient data?

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

#60

Post by fedgenerativeai »

fednongenai wrote: post #58
fedgenerativeai wrote: post #55

Earlier quoted context omitted.

OMG, hard to believe that this is from Flower founder's words. I am just a federated learning user and told the truth to the audience here. I have followed the FL community for more than 2 years, and I clearly feel that Flower has been copying and following FedML's product plan without any originality at all. These nonfactual attacks further makes me distrust on Flower team. You are ruining the reputation of YC. You…

*just a federated learning user* flashing a two-week old academic junk from the FedML team. Sus af. No f'ing way I am using a privacy product from the glorious land that gave us Tiktok and mass government surveillance.

Because that one is an important work for scalability, so I followed it closely.

I will stop here due to your disrespectful words. Good luck.

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