Forward-forward learning is a very interesting concept, and I think that in some cases it could definitely yield better results than distributed learning with backprop. It really depends on the size of the model, the latency of the connection, and the bandwidth of the slowest machine. I'm sure that in some cases it could be much faster, but I'm curious to know if there are any other advantages to using forward-forward learning over backprop for distributed learning.