  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.
