# Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning

arXiv: https://arxiv.org/abs/2009.05604 · HTML: https://mlsanity.com/paper/2009.05604/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 21 of 21)

- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Data Pricing in Machine Learning Pipelines](https://mlsanity.com/paper/2108.07915/) (2021)
- [A Contract Theory based Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2108.05568/) (2021)
- [Incentives in Private Collaborative Machine Learning](https://mlsanity.com/paper/2404.01676/) (2024)
- [Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective](https://mlsanity.com/paper/2207.12030/) (2022)
- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)
- [Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy](https://mlsanity.com/paper/2309.03437/) (2023)
- [Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design](https://mlsanity.com/paper/2211.04132/) (2022)
- [The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning](https://mlsanity.com/paper/2301.13336/) (2023)
- [On the Effect of Defections in Federated Learning and How to Prevent Them](https://mlsanity.com/paper/2311.16459/) (2023)
- [Incentivized Communication for Federated Bandits](https://mlsanity.com/paper/2309.11702/) (2023)
- [An Auction-based Marketplace for Model Trading in Federated Learning](https://mlsanity.com/paper/2402.01802/) (2024)
- [Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives](https://mlsanity.com/paper/2410.09107/) (2024)
- [Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation](https://mlsanity.com/paper/2410.15620/) (2024)
- [How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?](https://mlsanity.com/paper/2412.07813/) (2024)
- [Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It](https://mlsanity.com/paper/2509.02391/) (2025)
- [CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy](https://mlsanity.com/paper/2509.23190/) (2025)
- [Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View](https://mlsanity.com/paper/2607.23029/) (2026)

## Abstract (arXiv)

Federated Learning rests on the notion of training a global model distributedly on various devices. Under this setting, users' devices perform computations on their own data and then share the results with the cloud server to update the global model. A fundamental issue in such systems is to effectively incentivize user participation. The users suffer from privacy leakage of their local data during the federated model training process. Without well-designed incentives, self-interested users will be unwilling to participate in federated learning tasks and contribute their private data. To bridge this gap, in this paper, we adopt the game theory to design an effective incentive mechanism, which selects users that are most likely to provide reliable data and compensates for their costs of privacy leakage. We formulate our problem as a two-stage Stackelberg game and solve the game's equilibrium. Effectiveness of the proposed mechanism is demonstrated by extensive simulations.