Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems

Yongyi Tang, Kunlun Wang, Dusit Niyato, Wen Chen, George K. Karagiannidis

TL;DR

The efficacy of the proposed cooperative interference-based FL process has been verified through numerical analysis, which demonstrates that the integrated DT blockchain-assisted FL scheme significantly outperforms the benchmark schemes in terms of execution time, block optimization, and accuracy.

Abstract

In Industry 4.0 systems, a considerable number of resource-constrained Industrial Internet of Things (IIoT) devices engage in frequent data interactions due to the necessity for model training, which gives rise to concerns pertaining to security and privacy. In order to address these challenges, this paper considers a digital twin (DT) and blockchain-assisted federated learning (FL) scheme. To facilitate the FL process, we initially employ fog devices with abundant computational capabilities to generate DT for resource-constrained edge devices, thereby aiding them in local training. Subsequently, we formulate an FL delay minimization problem for FL, which considers both of model transmission time and synchronization time, also incorporates cooperative jamming to ensure secure synchronization of DT. To address this non-convex optimization problem, we propose a decomposition algorithm. In particular, we introduce upper limits on the local device training delay and the effects of aggregation jamming as auxiliary variables, thereby transforming the problem into a convex optimization problem that can be decomposed for independent solution. Finally, a blockchain verification mechanism is employed to guarantee the integrity of the model uploading throughout the FL process and the identities of the participants. The final global model is obtained from the verified local and global models within the blockchain through the application of deep learning techniques. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis, which demonstrates that the integrated DT blockchain-assisted FL scheme significantly outperforms the benchmark schemes in terms of execution time, block optimization, and accuracy.

Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems

TL;DR

The efficacy of the proposed cooperative interference-based FL process has been verified through numerical analysis, which demonstrates that the integrated DT blockchain-assisted FL scheme significantly outperforms the benchmark schemes in terms of execution time, block optimization, and accuracy.

Abstract

In Industry 4.0 systems, a considerable number of resource-constrained Industrial Internet of Things (IIoT) devices engage in frequent data interactions due to the necessity for model training, which gives rise to concerns pertaining to security and privacy. In order to address these challenges, this paper considers a digital twin (DT) and blockchain-assisted federated learning (FL) scheme. To facilitate the FL process, we initially employ fog devices with abundant computational capabilities to generate DT for resource-constrained edge devices, thereby aiding them in local training. Subsequently, we formulate an FL delay minimization problem for FL, which considers both of model transmission time and synchronization time, also incorporates cooperative jamming to ensure secure synchronization of DT. To address this non-convex optimization problem, we propose a decomposition algorithm. In particular, we introduce upper limits on the local device training delay and the effects of aggregation jamming as auxiliary variables, thereby transforming the problem into a convex optimization problem that can be decomposed for independent solution. Finally, a blockchain verification mechanism is employed to guarantee the integrity of the model uploading throughout the FL process and the identities of the participants. The final global model is obtained from the verified local and global models within the blockchain through the application of deep learning techniques. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis, which demonstrates that the integrated DT blockchain-assisted FL scheme significantly outperforms the benchmark schemes in terms of execution time, block optimization, and accuracy.

Paper Structure

This paper contains 27 sections, 2 theorems, 49 equations, 9 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. FL-Supported IIoT
  4. Digital Twin-Assisted FL
  5. Secure Synchronization of DT
  6. Blockchain-based FL in IIoT
  7. System Framework and Problem Formulation
  8. DT-Assisted Industrial Cyber-Physical Systems
  9. Blockchain with Conscensus
  10. DT-Assisted FL with Blockchain
  11. Local Device Classification
  12. Local Model Training for $GLD_i$ and $BLD_j$
  13. Local Model Upload for $GLD_i$ and DT Synchronization with $BLD_j$
  14. Average Aggregation and Model Upload for Group Coordinators
  15. Blockchain-Based Global Aggregation for Main Coordinator
  16. ...and 12 more sections

Key Result

Proposition 1

Problem P-GLD is a convex optimization problem with respect to $y$, $t^{\rm up}_{\rm G}$ and $\{q_i\}_{i\in \mathcal{M}}$.

Figures (9)

  • Figure 1: A multi-tier collaboration model of equipment in Industry 4.0.
  • Figure 2: The framework of DT-assisted FL with blockchain in multi-tier computing systems.
  • Figure 3: Convergence of $\{\lambda_i\}_{i\in \mathcal{M}}$ in Algorithm \ref{['alg:P']}.
  • Figure 4: Illustration of Algorithm 2 solving Problem P.
  • Figure 5: The advantage of Algorithm \ref{['alg:P']} in computation of time performance.
  • ...and 4 more figures

Theorems & Definitions (4)

  • Proposition 1
  • Proof 1
  • Proposition 2
  • Proof 2