# Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

arXiv: https://arxiv.org/abs/2202.10938 · HTML: https://mlsanity.com/paper/2202.10938/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://mlsanity.com/paper/2002.11711/) (2020)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture](https://mlsanity.com/paper/2010.07427/) (2020)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices](https://mlsanity.com/paper/1906.10893/) (2019)
- [Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling](https://mlsanity.com/paper/2105.14708/) (2021)
- [A blockchain-orchestrated Federated Learning architecture for healthcare consortia](https://mlsanity.com/paper/1910.12603/) (2019)
- [Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing](https://mlsanity.com/paper/2110.08671/) (2021)
- [Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2004.04104/) (2020)

## Built on by (most cited first, 13 of 13)

- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality](https://mlsanity.com/paper/2401.07558/) (2024)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content](https://mlsanity.com/paper/2406.08526/) (2024)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [Vision Language Model-Empowered Contract Theory for AIGC Task Allocation in Teleoperation](https://mlsanity.com/paper/2407.17428/) (2024)
- [Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning](https://mlsanity.com/paper/2503.07869/) (2025)
- [DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information](https://mlsanity.com/paper/2505.05842/) (2025)
- [A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization](https://mlsanity.com/paper/2310.09665/) (2023)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection](https://mlsanity.com/paper/2505.21219/) (2025)

## Abstract (arXiv)

Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages such as decentralization and privacy protection of raw data. However, there has been few research focusing on the allocation of resources for clients in BCFL. In the BCFL framework where the FL clients and the blockchain miners are the same devices, clients broadcast the trained model updates to the blockchain network and then perform mining to generate new blocks. Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to assign each client appropriate rewards for training and mining, and then the client will determine the amount of computing power to allocate for each subtask based on these rewards using the two-stage Stackelberg game. After analyzing the utilities of the model owner (MO) (i.e., the BCFL task publisher) and clients, we transform the game model into two optimization problems, which are sequentially solved to derive the optimal strategies for both the MO and clients. Further, considering the fact that local training related information of each client may not be known by others, we extend the game model with analytical solutions to the incomplete information scenario. Extensive experimental results demonstrate the validity of our proposed schemes.