FastDV: Scalable Model-Agnostic Data Valuation via Density Ratio Leave-One-Out

Leave-One-Out (LOO) data valuation (DV) quantifies the contribution of an individual sample through the finite utility change induced by its removal, providing useful guidance for data pruning, procurement, and selection. While model-agnostic DV avoids repeated model retraining by evaluating distributional utilities, existing methods typically rely on sensitivity, influence, or hierarchical approximations rather than explicit finite deletion. Moreover, most DV methods are developed for centralized settings, leaving independently computed values across multiple data holders generally incomparable. In this work, we propose FastDV, a scalable model-agnostic valuation framework. Specifically, we first derive an additive-statistic formulation that decomposes target set dependence into sample-wise additive statistics, making individual deletions analytically decomposable and distributed data partitions naturally composable. Guided by this principle, FastDV defines sample value through a Pearson-divergence-based density-ratio utility and achieves efficient LOO valuation through exact rank-one updates. Leveraging the partition composability of the above formulation, FastDV further extends to multi-party global valuation and produces globally comparable values without pooling raw client data. Extensive experiments on typical valuation tasks and data marketplaces demonstrate the effectiveness and scalability of FastDV. Notably, FastDV achieves up to 15.7$\times$ speedup over the fastest baseline on million-scale datasets.