AI RESEARCH
Principled Federated Random Forests for Heterogeneous Data
arXiv CS.LG
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ArXi:2602.03258v2 Announce Type: replace-cross Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning setting. Unlike most federated learning approaches, the piecewise-constant nature of RF prevents exact gradient-based optimization.