## 3 Use of DT and FL
### IIoT  
DT is widely used in IIoT systems. [PERSON] et al. elaborated on the definition of DT and the role of DT in IIoT systems. The authors also analyzed the decisions made by software architects regarding the architecture [46]. Similarly, the authors of [47] focused on the interoperability of DT for IIoT systems. The results showed that DT simulation of the development of the 3 GPP for ultra-reliable low latency (URLLC) reduces the delay and increases reliability [48].  
In [49], the authors proposed a DT model for basalt fiber production. They illustrated the smart digitization of the fiber industry using IIoT. [PERSON] et al. developed an artificial intelligence (AI) and extended reality (XR)-based DT for the management of complex systems. They also presented the advantages of using AI and XR for DT's improved training and performance [50].  
FL is also widely used for several purposes in IIoT networks. [PERSON] et al. demonstrated the data management by using a deep reinforcement learning (DRL)-based FL algorithm. The algorithm achieved 97% accuracy for the data management of the IIoT network [51]. [PERSON] et al. highlighted the importance of using FL to ensure the fairness and trustworthiness of the IIoT network [52].  
[PERSON] et al. presented a realistic dataset for cyber security. The dataset can be used for training FL models [53]. [PERSON] et al. presented a blockchain-enabled FL scheme for protecting the data using differential privacy and homomorphic encryption [54].  
[PERSON] et al. presented deadline, latency, and energy-efficient strategies and a BEFC scheme to ensure blockchain hashing and validation [55]. [PERSON] et al. proposed SecureIoT, an FL approach to avoid data breaches [56]. Similarly, the researchers demonstrated an FL and blockchain-enabled defensive transmission model and achieved 98% accuracy [57].  
[PERSON] et al. demonstrated federated transfer learning (FTL) for low computing power IIoT gadgets and achieved an accuracy of more than 70% for the KDD99 dataset [58]. In [59; 60],  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Theme** & **IIoT** & **IoV** & **IoD** & **Limitations** \\ \hline
[38] & FL for IoT & Yes & No & No & Only focused on the applications of FL \\  & & & & & for IoT \\
[39] & FL in mobile edge networks & No & No & No & Limited to application and challenges of FL \\  & & & & & for mobile edge networks \\
[40] & From distributed ML to FL & No & No & No & Evolution of FL from the distributed ML \\
[41] & FL for limited resources IoT devices & Yes & No & No & Comprehensive analysis of FL for the \\  & & & & & resource- constrained IoT device \\
[42] & Integration FL and IIoT & Yes & No & No & Discussed the fusion of FL and IIoT and \\  & & & & & challenges faced during fusion \\
[43] & DT for 3D printing & No & No & No & Reviewed the DT implementations and \\  & & & & & limitations for 3D printing \\
[44] & DT origin and future & No & No & Presented a comprehensive review of the & origin and future of DT \\
[45] & IoT Twins & Yes & No & No & Demonstrated the IoT twin’s implementation in Industry 4.0 \\ This survey & Application of DT and FL in IIoT, IoV and IoD & Yes & Yes & Focused on DT and FL at the same time for & IIoT, IoV, and IoD \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._the authors highlighted the use of FL for intrusion detection in context-aware IIoT networks and software-defined network (SDN)-enabled IIoT, respectively.  
The researchers presented an FL-based cyber-threat hunting model for IIoT networks based on blockchain technology [61]. [PERSON] et al. proposed a communication effective federated edge learning (FEL) algorithm for new radios in unlicensed spectrum (NRU)-based IIoT networks [62].  
[PERSON] et al. proposed a novel algorithm based on FTL for the authentication and privacy preservation of the IIoT network [63]. Table 3 presents the summary of the use of DT and FL in IIoT.