Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks

TL;DR

This paper presents a low-latency federated learning framework integrated with blockchain and digital twins in 6G networks, enhancing privacy, security, and efficiency for edge computing in IIoT environments.

Contribution

It introduces a novel Digital Twin Wireless Network architecture with blockchain-enabled federated learning and formulates an optimization for edge association using multi-agent reinforcement learning.

Findings

Improved system efficiency and reduced cost compared to benchmarks.

Enhanced data privacy and security through blockchain integration.

Effective edge association optimization balancing accuracy and latency.

Abstract

Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users, hinder the effective application of federated learning in IIoT. In this paper, we introduce the Digital Twin Wireless Networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system, and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multi-agent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning method.