mlsanityData to 7 Oct 2026

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

arXiv 2202.10938 ↗First posted 18 Feb 2022cs.CR cs.GT cs.LGcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 18 Feb 2022.

Builds on

15 of 43 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018How To Backdoor Federated Learningcited by 745
  3. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  4. 2020Threats to Federated Learning: A Surveycited by 171
  5. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  6. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  7. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  8. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  9. 2020BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecturecited by 20
  10. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  11. 2019Privacy-Preserving Blockchain-Based Federated Learning for IoT Devicescited by 12
  12. 2021Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Schedulingcited by 11
3 more
  1. 2019A blockchain-orchestrated Federated Learning architecture for healthcare consortiacited by 8
  2. 2021Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensingcited by 6
  3. 2020Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approachcited by 4

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Citing papers per year: 2023: 2; 2024: 7; 2025: 4.

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  1. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  2. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  3. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  4. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
  5. 2024FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Qualitycited by 4
  6. 2024IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Contentcited by 3
  7. 2024Blockchain Meets LLMs: A Living Survey on Bidirectional Integrationcited by 2
  8. 2024Vision Language Model-Empowered Contract Theory for AIGC Task Allocation in Teleoperationcited by 1
  9. 2025Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learningcited by 1
  10. 2025DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Informationcited by 1
  11. 2023A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimizationcited by 0
  12. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
1 more
  1. 2025Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selectioncited by 0