https://arxiv.org/ has a ton of papers on it.

Do you have some good search terms to get started down the rabbit hole?

This one is a few years old, but seems interesting: https://arxiv.org/abs/1802.05799v2

I too would like to go down this rabbit hole. I am going to poke around using the terms “distributed learning” and “federated learning” (They’re different areas, but somewhat related as far as I understand).

Probably the biggest recent result: https://arxiv.org/abs/2209.04836 (author thread: https://twitter.com/SamuelAinsworth/status/15697194946455265...)

See also: https://github.com/learning-at-home/hivemind

and more to OP's incentive structure: https://docs.bittensor.com/

Latter two intend to beat latency with Mixture-of-Expert models (MoEs). If the results of the former hold, it shows that with a simple algorithmic transformation you can merge two independently trained models in weight-space and have performance functionally equivalent to a model trained monolithically.

A broad covering term is MLOps (like DevOps).

https://en.wikipedia.org/wiki/MLOps

Armed with that term, we get (haven’t read):

Machine Learning Operations (MLOps): Overview, Definition, and Architecture

https://arxiv.org/abs/2205.02302

This initiative is getting some pretty good practical results https://arxiv.org/abs/2209.01188, taking a look at their citations should lead you to other examples in the field