I did my undergrad internship on federated learning. I was tasked with implementing in a simulator different federated algorithms, so to have a way to compare them in a meaningful way. The last that had to be implemented was FedMA. We didn't manage to do it. That algorithm is absolutely devilish. Every issue that I solved made other two issue arise, and neither my supervisors could help. The sheer idea of matching neurons in different networks might (and does) make sense, but the way the approximate costs are calculated require other 2/3 math papers that I could follow for only the first lines of the abstract. I'm happy for the time I spent in my internship there. I'm also happy it's over

The general understanding of how it works is surprisingly easy though, you can find the paper here https://arxiv.org/abs/2002.06440

The whole point of deploying such things on-prem is air-gapping it from Google and its "learning".