Napster/BitTorrent for machine learning?
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I did a prototype implementation of a “network of ML networks” - an internet-like protocol for federated learning where nodes can discover, join, and migrate between different learning groups based on performance metrics (Repo: https://github.com/bluebbberry/MyceliumNetServer). It's build on Flower AI.
Want do you think of this? It could be used to build a Napster/BitTorrent-like app on this to collaboratively train and share arbitrary machine learning models with other people while keeping data private and only sharing gradients instead of whole models to save bandwidth. Would this be a good counter-weight for big AI companies or actually make things worse?
Would love to hear your opinion
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I did a prototype implementation of a “network of ML networks” - an internet-like protocol for federated learning where nodes can discover, join, and migrate between different learning groups based on performance metrics (Repo: https://github.com/bluebbberry/MyceliumNetServer). It's build on Flower AI.
Want do you think of this? It could be used to build a Napster/BitTorrent-like app on this to collaboratively train and share arbitrary machine learning models with other people while keeping data private and only sharing gradients instead of whole models to save bandwidth. Would this be a good counter-weight for big AI companies or actually make things worse?
Would love to hear your opinion
Can you help me understand the use case?
Is it intended to be a variant of Flower AI that can be used in an adversarial environment?
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I did a prototype implementation of a “network of ML networks” - an internet-like protocol for federated learning where nodes can discover, join, and migrate between different learning groups based on performance metrics (Repo: https://github.com/bluebbberry/MyceliumNetServer). It's build on Flower AI.
Want do you think of this? It could be used to build a Napster/BitTorrent-like app on this to collaboratively train and share arbitrary machine learning models with other people while keeping data private and only sharing gradients instead of whole models to save bandwidth. Would this be a good counter-weight for big AI companies or actually make things worse?
Would love to hear your opinion
What would a use case look like?
I assume that the latency will make it impractical to train something that's LLM-sized. But even for something small, wouldn't a data center be more efficient?
