mlsanityData to 14 Oct 2026

Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game

arXiv 2502.06909 ↗First posted 10 Feb 2025cs.LG cs.AI cs.GTcited by 0

Not on the map. Why?

Abstract (arXiv)

The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing resources, improving efficiency and scalability. Efficient IIoT operations require a trade-off between model quality and training latency. Consequently, a primary challenge of FL in IIoT is to optimize overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency for meta-computing. Additionally, the satisfaction function is incorporated into the utility function to incentivize IIoT nodes to participate in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for IIoT. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves utility by at least 23.7% compared to existing FL schemes without compromising model accuracy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

13 of 38 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  6. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  7. 2020Age of Information: An Introduction and Surveycited by 70
  8. 2022Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoIcited by 16
  9. 2021Federated Learning for Industrial Internet of Things in Future Industriescited by 14
  10. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  11. 2021Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoTcited by 9
  12. 2023Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Reviewcited by 8
1 more
  1. 2023Meta Computingcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Incentive-Based Federated Learning: Architectural Elements and Future Directions
  2. 2025A Service-Oriented Adaptive Hierarchical Incentive Mechanism for Federated Learning
  3. 2024Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things
  4. 2025(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems
  5. 2025Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning
  6. 2026FORLER: Federated Offline Reinforcement Learning with Q-Ensemble and Actor Rectification
  7. 2025Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
  8. 2021Federated Ensemble Model-based Reinforcement Learning in Edge Computing