openfl VS PySyft

We have petals.ml for distributing running a single model large model for inference. We have [flower](flower.dev),tensor flow federated,openfl, etc for building federated learning.

Code for https://arxiv.org/abs/2105.06413 found: https://github.com/intel/openfl

https://github.com/OpenMined/PySyft - Federated Learning data science

Incentives are much harder but smart contracts can handle the tech part.

Going this route eventually you quickly have "quantum AI app store" and your system of government is a 12GB download. Can't even say if it's a good idea compared to e.g. anarcho-primitivism.

Something that maybe can help keeping sensor specs secret while still getting critical information out: https://github.com/OpenMined/PySyft

Love this kind of project. To me this is just like https://github.com/open-quantum-safe/oqs-demos/ or https://github.com/OpenMined/PySyft or even k3s so often mentioned in this sub in the sense that I personally don't have a need for it. Yet I find it amazing that us, random curious geeks, have access to this kind of mind blowing technologies for basically free.

Or do I have to actually simulate the whole client server thing because thats how these frameworks do it - Flower and Pysyft .

Some of the technologies which could be integrated include differential privacy, distributed online machine learning, misinformation resilience and multi-party computation, all within the context of smart contracts and bioinformatics.

I think this is how it will be done. Look up PySift for how we can extract high-level insights from private datasets while preserving granular privacy.

What you are talking about is privacy-focused fed ML. Google FLOC is actually trying to achieve something similar. If you are interested in building something for yourself, check this out. https://github.com/OpenMined/PySyft