Blockchain based federated learning is a distributed learning scheme that allows model training without participants sharing their local data sets. We will review the basics of blockchains and how to use them for training a model based on the proof-of-stake consensus mechanism. We will focus on a newly developed weight aggregation schema employed by validators to guard against attacks and with a great performance for non-iid data. A convergence analysis will be discussed, and several numerical experiments presented.