your topic subject was interesting - refer to engaging ML in consensus process, but I found nothing about it in content. so I really need to focus to the subject and discuss it more in depth. do you know, the human brain is the best practice that ever developed about node-consensus in the universe? every decision that we make as human, is coming from a perfect consensus algorithm that we try to emulate it by Neural Networks (NN) in AI. just with Bio-NN there is always one stake-holder (the owner of the brain) that the whole process belongs (is centralized) to him, but in Artificial-NN the outcome of the whole process has its own impact on all stake-holders and this makes it complicated.

NN always provides a Black-Box processing unit, so nobody really could understand what would be the next step of one person and if you put a cryptocurrency in the hand of an Artificial-NN, you may have a blockchain that after many years, somehow may gives its vote to a traitor node and nobody could stop it!!

BUT, you could learn something really useful if you ask: "what would be the main characteristics of an advanced consensus algorithm?". so, just simply take a look at the human brain, which is drastically DECENTRALIZED and powerful by its PARALLEL-PROCESSING-UNITS that SHARE their jobs (not in the race of "who is doing it first..") including HUGE-STORAGE-SPACE and MULTI-ROUTES among nodes including HIGH-BANDWIDTH between them, with the least POWER-CONSUMPTION rates!

because of the black-box part that I have mentioned above, we can not provide a consensus algorithm that works fully by ML, but based on lessons that we could learn from human brain, now we know what kind of characteristics for a consensus algorithm we should look for.