Geodesic-SVDD Latent Mixup for One-Shot Distributed Tabular Learning
Read the original on arXiv Machine Learning →Geodesic‑SVDD introduces a distributed learning framework for structured tabular data that transforms each client’s task into a one‑class density estimation on a hypersphere, enabling aggregation of local models as probabilistic manifolds in a single communication round. The method employs a Residual Spherical Encoder and a Geodesic Latent Mixup to enforce strict hyperspherical constraints, preventing manifold collapse, and proposes a One‑Shot Product‑of‑Experts protocol for privacy‑preserving aggregation without iterative synchronization. Experiments on five tabular benchmarks and varying label skew levels show that Geodesic‑SVDD achieves the highest mean AUROC among seven federated methods, surpasses federated tree ensembles by up to 21 AUROC points, and its One‑Shot variant matches centralized Deep SVDD under IID and mild skew conditions.
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