# Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives

arXiv: https://arxiv.org/abs/2410.09107 · HTML: https://mlsanity.com/paper/2410.09107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Collaborative Fairness in Federated Learning](https://mlsanity.com/paper/2008.12161/) (2020)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2109.02053/) (2021)
- [Collaborative Machine Learning with Incentive-Aware Model Rewards](https://mlsanity.com/paper/2010.12797/) (2020)
- [Fair Federated Medical Image Segmentation via Client Contribution Estimation](https://mlsanity.com/paper/2303.16520/) (2023)
- [FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC](https://mlsanity.com/paper/2002.09699/) (2020)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)
- [Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning](https://mlsanity.com/paper/2009.05604/) (2020)
- [Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.10373/) (2023)
- [When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System](https://mlsanity.com/paper/2102.10109/) (2021)

## Built on by (most cited first, 1 of 1)

- [Owen Sampling Accelerates Contribution Estimation in Federated Learning](https://mlsanity.com/paper/2508.21261/) (2025)

## Abstract (arXiv)

In recent years, research on the data trading market has been continuously deepened. In the transaction process, there is an information asymmetry process between agents and sellers. For sellers, direct data delivery faces the risk of privacy leakage. At the same time, sellers are not willing to provide data. A reasonable compensation method is needed to encourage sellers to provide data resources. For agents, the quality of data provided by sellers needs to be examined and evaluated. Otherwise, agents may consume too much cost and resources by recruiting sellers with poor data quality. Therefore, it is necessary to build a complete delivery process for the interaction between sellers and agents in the trading market so that the needs of sellers and agents can be met. The federated learning architecture is widely used in the data market due to its good privacy protection. Therefore, in this work, in response to the above challenges, we propose a transaction framework based on the federated learning architecture, and design a seller selection algorithm and incentive compensation mechanism. Specifically, we use gradient similarity and Shapley algorithm to fairly and accurately evaluate the contribution of sellers, and use the modified UCB algorithm to select sellers. After the training, fair compensation is made according to the seller's participation in the training. In view of the above work, we designed reasonable experiments for demonstration and obtained results, proving the rationality and effectiveness of the framework.