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

Xiaohuan Li, Shaowen Qin, Xin Tang, Jiawen Kang, Jin Ye, Zhonghua Zhao, Yusi Zheng, Dusit Niyato

TL;DR

The paper tackles the challenge of balancing model quality and training latency in IIoT federated learning by introducing MEFL, a meta-computing enhanced FL framework that uses a Satisfaction metric to quantify node contributions. It formulates a two-stage Stackelberg incentive game between the server and IIoT nodes and employs a DRL-based MADDPG approach to learn the Stackelberg equilibrium without sharing private information. Key contributions include the satisfaction-based utility design, a provably unique Stackelberg equilibrium, and a DRL method that achieves near-SE performance with robust adaptability to dynamic IIoT conditions. Results show the proposed scheme yields substantial utility gains under budget constraints while maintaining or improving FL accuracy compared to existing schemes.

Abstract

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.

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

TL;DR

The paper tackles the challenge of balancing model quality and training latency in IIoT federated learning by introducing MEFL, a meta-computing enhanced FL framework that uses a Satisfaction metric to quantify node contributions. It formulates a two-stage Stackelberg incentive game between the server and IIoT nodes and employs a DRL-based MADDPG approach to learn the Stackelberg equilibrium without sharing private information. Key contributions include the satisfaction-based utility design, a provably unique Stackelberg equilibrium, and a DRL method that achieves near-SE performance with robust adaptability to dynamic IIoT conditions. Results show the proposed scheme yields substantial utility gains under budget constraints while maintaining or improving FL accuracy compared to existing schemes.

Abstract

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.

Paper Structure

This paper contains 18 sections, 1 theorem, 23 equations, 11 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. FL in IIoT
  4. Incentive Schemes in IIoT
  5. Game Theoretic and DRL Approaches in IIoT
  6. System Model
  7. Satisfaction: Quality Control for IIoT
  8. Satisfaction-aware incentives scheme
  9. Utility Model and Problem Formulation
  10. Stackelberg Game Analysis
  11. DRL-based Stackelberg Game
  12. DRL for Stackelberg Game
  13. MADDPG Algorithm
  14. Simulation Results
  15. Satisfaction and Utility Analysis
  16. ...and 3 more sections

Key Result

Theorem 1

There exists a unique stackelberg equilibrium in the proposed game $(\theta _{i}^{*},r_{i}^{*})$.

Figures (11)

  • Figure 1: The MEFL framework for IIoT. The framework enables resource scheduling and task management for IIoT devices, where the server employs a Satisfaction-aware incentive mechanism to coordinate nodes for efficient task execution.
  • Figure 2: DRL algorithm for Stackelberg game.
  • Figure 3: Details of the DRL Controller.
  • Figure 4: Satisfaction for different update cycle.
  • Figure 5: Server utility for different schemes and numbers of selected nodes.
  • ...and 6 more figures

Theorems & Definitions (3)

  • Definition 1
  • Theorem 1
  • proof