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
Congrats on the launch Flower team!
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!
https://arxiv.org/abs/2105.06413 https://openfl.readthedocs.io/en/latest/index.html https://gramineproject.io/
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.
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.