Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks (2011.09902v1)

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.

• The paper presents a blockchain-empowered federated learning framework integrating digital twins to boost data security and reliability in 6G networks.
• The paper formulates an optimization problem addressed via multi-agent reinforcement learning to balance learning accuracy and time cost for improved edge association.
• The paper demonstrates through numerical evaluations that the proposed model outperforms conventional methods, enhancing efficiency in IIoT edge computing.

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

In the paper “Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks,” the authors address the integration of emerging technologies, specifically Digital Twins (DT), Blockchain, and Federated Learning (FL), within the context of 6th Generation (6G) wireless networks. This paper proposes a robust framework leveraging these technologies to improve efficiency and security in edge computing for the Industrial Internet of Things (IIoT).

Key Contributions

The authors introduce the Digital Twin Wireless Networks (DTWN) model that incorporates digital twins into wireless networks to tackle limitations associated with conventional federated learning—such as unreliable communication, resource constraints, and trust issues among users. The framework aims to enhance real-time data processing capabilities by migrating computations to the edge.

1. Blockchain-empowered Federated Learning: The paper presents a blockchain-based FL framework in DTWN to ensure data reliability, security, and privacy. The integration of blockchain technology addresses mutual trust deficiencies among users by securely recording federated learning parameters, thus reinforcing system reliability.
2. Optimization Problem: The authors formulate an optimization problem to balance learning accuracy and time cost, addressing digital twin association, training data batch size, and bandwidth allocation. A multi-agent reinforcement learning algorithm is employed for deriving an optimal solution.
3. Numerical Evaluation: The proposed scheme's efficiency and cost-effectiveness are demonstrated using a real-world dataset, showcasing performance improvements over established benchmark methods.

Discussion

The integration of digital twins in wireless networks facilitates enhanced AI algorithm efficiency by mitigating the adverse effects of unreliable communication channels. The DTWN model enables real-time synchronization between physical IIoT systems and their digital counterparts, fostering a more responsive data processing environment. Consequently, this integration could significantly optimize resource utilization in edge computing applications.

Blockchain implementation within this framework addresses security and trust concerns, pivotal for federated learning, notably in scenarios involving untrusted participants. By recording parameters on a permissioned blockchain, the framework enhances not only data privacy but also the overall system's integrity and trustworthiness.

The paper’s multi-agent reinforcement learning solution for the resource allocation problem signifies an innovative step towards efficient edge association in 6G networks. By optimizing digital twin association strategies, batch sizes, and bandwidth distribution, the proposed solution minimizes the time cost while maintaining required learning accuracy.

Implications and Future Directions

This research has significant implications for IIoT, where ensuring timely and secure data processing is crucial. The proposed framework may lead to more resilient and adaptable 6G networks capable of supporting complex and latency-sensitive applications in smart cities and intelligent transportation systems.

Future work could explore adaptive models that further optimize resource allocation under dynamically changing network conditions, perhaps using deep learning techniques or broader-scale simulations. Expanding the transparency and decentralization features of blockchain within this framework may also present opportunities for enhancing data governance in distributed networks.

Overall, this paper offers a promising pathway toward addressing some of the fundamental challenges in implementing federated learning in 6G network environments, laying the groundwork for advancements in edge computing and IIoT ecosystems.