Benchmarking Federated Learning & Knowledge Distillation for Point Cloud Classification

Deploying 3D point cloud analysis in privacy-sensitive and resource-constrained settings faces two coupled barriers: data cannot be centralized for training, and the trained model must run on limited edge hardware. We present a multi-seed benchmark that jointly evaluates federated learning (FL) and knowledge distillation (KD) for 3D point cloud classification. It spans thirteen FL algorithms and ten KD objectives, supporting their full 130-pair teacher–objective cross-product per dataset; every standardized configuration is repeated over three random seeds for 504 training runs in total, with the complete combined grid evaluated at multi-seed scale on the clinical dataset. We characterize federated degradation and the combined-pipeline pitfall on ModelNet40, then validate them on a real-world clinical craniosynostosis dataset of patient head shapes, where the privacy and edge-deployment stakes are concrete. We report three findings. First, under extreme non-independent and identically distributed (non-IID) label skew, standalone FL degrades sharply: on ModelNet40 the strongest method reaches only 76.32% against a 92.26% centralized reference, on the clinical data the best reaches 75.83% against 100%, and the four server-side optimizers collapse to near the chance level; the best algorithm differs by dataset, so none is universally robust. Second, distillation compresses the teacher into a student 74.51% smaller and roughly twice as fast at inference, with five of the seven objectives evaluated on ModelNet40 matching or surpassing the 92.44% teacher. Third, the combined pipeline exposes an evaluation pitfall: when distillation keeps a hard-label cross-entropy term on a labeled proxy split, a collapsed federated teacher at 8.50% paired with Logit-MSE still yields a 92.94% student. This 84.4-point gap reflects the proxy labels rather than the federated model, and the hard-label term reuses the very labels whose privacy motivated federation. Objectives without a hard-label term instead track teacher quality (r ≈ 0.99 on the clinical grid) and collapse when the teacher does. We therefore recommend evaluating FL-KD pipelines with label-free distillation, so that the reported accuracy reflects the federated teacher rather than the proxy.