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This arXiv preprint proposes Geodesic-SVDD, a federated method that models each client's data as one-class density estimation on a hypersphere and aggregates the local models in a single communication round. Its reported gains are benchmark results on five tabular datasets with label skew, including a claimed AUROC advantage over federated tree ensembles on Covertype. Readers should weigh those as preliminary, since they come from a single new paper and have not been independently replicated.
Geodesic-SVDD Latent Mixup for One-Shot Distributed Tabular Learning
arXiv:2610.33619v1 Announce Type: new Abstract: Distributed Learning on structured data is a key and widely adopted approach in privacy-sensitive sectors, ranging from financial fraud prevention to federated healthcare diagnostics. While Gradient Boosted Decision Trees are widely used in centralized settings due to their efficiency, they are incompatible with standard gradient-based federated learning protocols because of their non-differentiability. Conversely, standard deep classifiers that are amenable to federated optimization often suffer from catastrophic forgetting under label distribution skew. In this work, to address these gaps we propose Geodesic-SVDD, a framework for Distributed Spherical Prototype Learning that recasts each client's task as one-class density estimation on the hypersphere, so that local models can be aggregated as probabilistic manifolds in a single round. Specifically, for each client, we employ a Residual Spherical Encoder and Geodesic Latent Mixup that enforces strict hyperspherical constraints ($\|\phi(x)\| \equiv 1$), preventing the manifold collapse observed in linear approximations. Furthermore, leveraging this insight, we propose a novel One-Shot "Product of Experts" protocol that enables privacy-preserving aggregation without iterative synchronization. Finally, we mechanistically analyze the trade-off between interpolation strategies, demonstrating that while linear mixup offers an enticing computational shortcut, it destabilizes optimization gradients by orders of magnitude ($10^9\times$). Across five tabular benchmarks and four levels of label skew, Geodesic-SVDD attains the highest mean AUROC among seven federated methods, outperforms federated tree ensembles by up to 21 AUROC points on Covertype, and its One-Shot variant matches centralized Deep SVDD under IID and mild skew with a single communication round.
Abstract:Distributed Learning on structured data is a key and widely adopted approach in privacy-sensitive sectors, ranging from financial fraud prevention to federated healthcare diagnostics. While Gradient Boosted Decision Trees are widely used in centralized settings due to their efficiency, they are incompatible with standard gradient-based federated learning protocols because of their non-differentiability. Conversely, standard deep classifiers that are amenable to federated optimization often suffer from catastrophic forgetting under label distribution skew. In this work, to address these gaps we propose Geodesic-SVDD, a framework for Distributed Spherical Prototype Learning that recasts each client's task as one-class density estimation on the hypersphere, so that local models can be aggregated as probabilistic manifolds in a single round. Specifically, for each client, we employ a Residual Spherical Encoder and Geodesic Latent Mixup that enforces strict hyperspherical constraints ($\|\phi(x)\| \equiv 1$), preventing the manifold collapse observed in linear approximations. Furthermore, leveraging this insight, we propose a novel One-Shot "Product of Experts" protocol that enables privacy-preserving aggregation without iterative synchronization. Finally, we mechanistically analyze the trade-off between interpolation strategies, demonstrating that while linear mixup offers an enticing computational shortcut, it destabilizes optimization gradients by orders of magnitude ($10^9\times$). Across five tabular benchmarks and four levels of label skew, Geodesic-SVDD attains the highest mean AUROC among seven federated methods, outperforms federated tree ensembles by up to 21 AUROC points on Covertype, and its One-Shot variant matches centralized Deep SVDD under IID and mild skew with a single communication round.
Machine Learning (cs.LG)
arXiv:2610.33619 [cs.LG]
(or arXiv:2610.33619v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.33619 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
2026 International Joint Conference on Neural Networks (IJCNN), Maastricht, Netherlands, 2026, pp. 1-8
https://doi.org/10.1109/IJCNN69854.2026.11728825 Focus to learn more DOI(s) linking to related resources
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