[R] Has there been any work on alternating minimization methods for training neural nets?

What I mean is training one layer at a time by freezing all but one and repeating this until the weights converge and give the best loss. I've seen narrow work on this in 2019 but it's surprising it hasn't been done before or at least explored. If there have been studies on this, what's the basic conclusion?