Towards Neuro-Symbolic Federated Tree Learning
Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a model while keeping their data localized, thus preserving privacy. While most FL approaches focus on deep learning models, there is a growing interest in exploring alternative models that can offer interpretability and efficiency. This paper presents TreeClimber, a novel neuro-symbolic approach for training decision tree models in a federated setting using gradient-free optimization techniques. TreeClimber embeds data into a subsymbolic latent space via a randomized embedding, from which decision trees are reconstructed through a neural architecture. Trees are optimized in the embedding space in a federated setting using a zeroth-order approximation of the gradient, allowing for effective training of interpretable decision models without direct access to data. Experimental results demonstrate the effectiveness of TreeClimber in enabling secure distributed training of decision trees without sharing raw information about local datasets, achieving competitive performance compared to traditional centralized training methods.