# Data Valuation and Selection in a Federated Model Marketplace

arXiv: https://arxiv.org/abs/2509.18104 · HTML: https://mlsanity.com/paper/2509.18104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Estimating Training Data Influence by Tracing Gradient Descent](https://mlsanity.com/paper/2002.08484/) (2020)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [Theoretical Analysis of Domain Adaptation with Optimal Transport](https://mlsanity.com/paper/1610.04420/) (2016)
- [Robust Federated Learning: The Case of Affine Distribution Shifts](https://mlsanity.com/paper/2006.08907/) (2020)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [LAVA: Data Valuation without Pre-Specified Learning Algorithms](https://mlsanity.com/paper/2305.00054/) (2023)
- [Model-based Pricing for Machine Learning in a Data Marketplace](https://mlsanity.com/paper/1805.11450/) (2018)
- [Learning Wasserstein Embeddings](https://mlsanity.com/paper/1710.07457/) (2017)
- [Data Valuation and Detections in Federated Learning](https://mlsanity.com/paper/2311.05304/) (2023)
- [Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources](https://mlsanity.com/paper/2307.02460/) (2023)
- [FedImpro: Measuring and Improving Client Update in Federated Learning](https://mlsanity.com/paper/2402.07011/) (2024)
- [An Optimal Transport Approach to Personalized Federated Learning](https://mlsanity.com/paper/2206.02468/) (2022)
- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [On the Generalization of Wasserstein Robust Federated Learning](https://mlsanity.com/paper/2206.01432/) (2022)
- [Federated Wasserstein Distance](https://mlsanity.com/paper/2310.01973/) (2023)
- [Wasserstein Markets for Differentially-Private Data](https://mlsanity.com/paper/2412.02609/) (2024)

## Abstract (arXiv)

In the era of Artificial Intelligence (AI), marketplaces have become essential platforms for facilitating the exchange of data products to foster data sharing. Model transactions provide economic solutions in data marketplaces that enhance data reusability and ensure the traceability of data ownership. To establish trustworthy data marketplaces, Federated Learning (FL) has emerged as a promising paradigm to enable collaborative learning across siloed datasets while safeguarding data privacy. However, effective data valuation and selection from heterogeneous sources in the FL setup remain key challenges. This paper introduces a comprehensive framework centered on a Wasserstein-based estimator tailored for FL. The estimator not only predicts model performance across unseen data combinations but also reveals the compatibility between data heterogeneity and FL aggregation algorithms. To ensure privacy, we propose a distributed method to approximate Wasserstein distance without requiring access to raw data. Furthermore, we demonstrate that model performance can be reliably extrapolated under the neural scaling law, enabling effective data selection without full-scale training. Extensive experiments across diverse scenarios, such as label skew, mislabeled, and unlabeled sources, show that our approach consistently identifies high-performing data combinations, paving the way for more reliable FL-based model marketplaces.