Hello everyone, I've noticed that many participants are using decision tree-based models. I'm wondering how federated learning could be implemented with them, since—unlike neural networks—decision trees don't rely on weights that can be averaged across clients. It seems to present a lot of challenges, especially when it comes to synchronizing tree structures or securely evaluating splits. Could someone clarify this for me? Am I missing something?