# FedToken: Tokenized Incentives for Data Contribution in Federated Learning

arXiv: https://arxiv.org/abs/2209.09775 · HTML: https://mlsanity.com/paper/2209.09775/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Personalized Federated Learning with First Order Model Optimization](https://mlsanity.com/paper/2012.08565/) (2020)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC](https://mlsanity.com/paper/2002.09699/) (2020)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach](https://mlsanity.com/paper/2009.10269/) (2020)
- [A Crowdsourcing Framework for On-Device Federated Learning](https://mlsanity.com/paper/1911.01046/) (2019)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization](https://mlsanity.com/paper/1309.2375/) (2013)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data](https://mlsanity.com/paper/2112.02870/) (2021)
- [A Contribution-based Device Selection Scheme in Federated Learning](https://mlsanity.com/paper/2203.05369/) (2022)

## Built on by (most cited first, 9 of 9)

- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2303.06546/) (2023)
- [Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation](https://mlsanity.com/paper/2305.11633/) (2023)
- [Privacy-Aware Data Acquisition under Data Similarity in Regression Markets](https://mlsanity.com/paper/2312.02611/) (2023)
- [Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners](https://mlsanity.com/paper/2505.12010/) (2025)
- [Scalable Data Point Valuation in Decentralized Learning](https://mlsanity.com/paper/2305.01657/) (2023)
- [WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning](https://mlsanity.com/paper/2506.20518/) (2025)
- [Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation](https://mlsanity.com/paper/2603.20873/) (2026)
- [SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks](https://mlsanity.com/paper/2605.28764/) (2026)

## Abstract (arXiv)

Incentives that compensate for the involved costs in the decentralized training of a Federated Learning (FL) model act as a key stimulus for clients' long-term participation. However, it is challenging to convince clients for quality participation in FL due to the absence of: (i) full information on the client's data quality and properties; (ii) the value of client's data contributions; and (iii) the trusted mechanism for monetary incentive offers. This often leads to poor efficiency in training and communication. While several works focus on strategic incentive designs and client selection to overcome this problem, there is a major knowledge gap in terms of an overall design tailored to the foreseen digital economy, including Web 3.0, while simultaneously meeting the learning objectives. To address this gap, we propose a contribution-based tokenized incentive scheme, namely textttFedToken, backed by blockchain technology that ensures fair allocation of tokens amongst the clients that corresponds to the valuation of their data during model training. Leveraging the engineered Shapley-based scheme, we first approximate the contribution of local models during model aggregation, then strategically schedule clients lowering the communication rounds for convergence and anchor ways to allocate affordable tokens under a constrained monetary budget. Extensive simulations demonstrate the efficacy of our proposed method.