# A Comprehensive Survey of Incentive Mechanism for Federated Learning

arXiv: https://arxiv.org/abs/2106.15406 · HTML: https://mlsanity.com/paper/2106.15406/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 58 references that are in the map.
- First posted on arXiv 27 Jun 2021.

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

- [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)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [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)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)
- [FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://mlsanity.com/paper/2002.11711/) (2020)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://mlsanity.com/paper/2004.03877/) (2020)
- [Joint Service Pricing and Cooperative Relay Communication for Federated Learning](https://mlsanity.com/paper/1811.12082/) (2018)
- [Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning](https://mlsanity.com/paper/2009.05604/) (2020)
- [Estimation of Individual Device Contributions for Incentivizing Federated Learning](https://mlsanity.com/paper/2009.09371/) (2020)
- [Incentives for Federated Learning: a Hypothesis Elicitation Approach](https://mlsanity.com/paper/2007.10596/) (2020)
- [A VCG-based Fair Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2008.06680/) (2020)
- [FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism](https://mlsanity.com/paper/2012.10566/) (2020)
- [From Server-Based to Client-Based Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/1909.08329/) (2019)
- [Mechanism Design for Multi-Party Machine Learning](https://mlsanity.com/paper/2001.08996/) (2020)
- [2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments](https://mlsanity.com/paper/2011.07516/) (2020)
- [Incentive Mechanism Design for Distributed Coded Machine Learning](https://mlsanity.com/paper/2012.08715/) (2020)

## Built on by (most cited first, 30 of 38)

- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [FS-Real: Towards Real-World Cross-Device Federated Learning](https://mlsanity.com/paper/2303.13363/) (2023)
- [Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study](https://mlsanity.com/paper/2303.17114/) (2023)
- [FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs](https://mlsanity.com/paper/2309.01172/) (2023)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems](https://mlsanity.com/paper/2201.02775/) (2022)
- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation](https://mlsanity.com/paper/2304.07981/) (2023)
- [FedDebug: Systematic Debugging for Federated Learning Applications](https://mlsanity.com/paper/2301.03553/) (2023)
- [Strategic Data Sharing between Competitors](https://mlsanity.com/paper/2305.16052/) (2023)
- [FedFair^3: Unlocking Threefold Fairness in Federated Learning](https://mlsanity.com/paper/2401.16350/) (2024)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024)
- [A Framework for Incentivized Collaborative Learning](https://mlsanity.com/paper/2305.17052/) (2023)
- [Hierarchical Federated Learning Incentivization for Gas Usage Estimation](https://mlsanity.com/paper/2307.00233/) (2023)
- [Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach](https://mlsanity.com/paper/2401.11836/) (2024)
- [On the Volatility of Shapley-Based Contribution Metrics in Federated Learning](https://mlsanity.com/paper/2405.08044/) (2024)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [DICE: Data Influence Cascade in Decentralized Learning](https://mlsanity.com/paper/2507.06931/) (2025)
- [Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective](https://mlsanity.com/paper/2302.08976/) (2023)
- [Federated Learning Incentive Mechanism under Buyers' Auction Market](https://mlsanity.com/paper/2309.05063/) (2023)
- [Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review](https://mlsanity.com/paper/2405.13832/) (2024)
- [Incentivizing Inclusive Contributions in Model Sharing Markets](https://mlsanity.com/paper/2505.02462/) (2025)
- [Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction](https://mlsanity.com/paper/2202.05139/) (2022)
- [TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments](https://mlsanity.com/paper/2211.00735/) (2022)

## Abstract (arXiv)

Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning process. Thus, it is quite crucial to inspire more participants to contribute their valuable resources with some payments for federated learning. In this paper, we present a comprehensive survey of incentive schemes for federate learning. Specifically, we identify the incentive problem in federated learning and then provide a taxonomy for various schemes. Subsequently, we summarize the existing incentive mechanisms in terms of the main techniques, such as Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain. By reviewing and comparing some impressive results, we figure out three directions for the future study.