## 4 Proposed System Design Overview
### Optimised FTL-Assisted Edge IDS Learning
#### 4.3.2 Global Model Generation with Relational Knowledge Transfer  
Following local model learning, it is crucial to transfer or update the specified local parameters to the server in order to build a global shared model in the FTL-based IDS. The TL model aims to transfer the local parameters generated with gradient-based weights from the edges to the central server. The TL partially retrains the predefined learning of the central server, which limits the complexity of the algorithm and increases the accuracy of global model generation. In the FT-CID model, the local learning models comprise local parameters with logits between 0 and 1, which are considered relational knowledge values. In the local learning model, parameter transfer involves the transfer of the learning model parameters, logits, and weights. The local learning model parameters and logits mainly refer to the patterns learned from the known and unknown types of intrusions of every RPL-IIoT domain, and the weights denote the importance of the local learning model parameter for the results. The logit of each local model is updated to the security sensor server using a TL algorithm. The TL process with parameter transfer and relational knowledge transfer during the local-to-global updating and global-to-local optimisation processes is shown in Figures 2 and 3. After global parameter modelling, the FT-CID model carries the global intrusion patterns in terms of new logits and weights to the edges for local learning model optimisation with the assistance of the TL model. The FT-CID model builds the global shared model with the weighted average of the local learning models using FedAVG for the heterogeneous IIoT edges. The FedAVG computes the differing importance of weight degree values for each local model. Hence, the edge of the IIoT network has a different importance value in the FT-CID model based on the impact of the local learning model parameters on the global model and the gradient. The FedAVG then computes the standard importance degree for each edge of the IIoT network based on the local model contribution parameter weights and gradients and thus optimises the updating of the global shared model. The local model receives high weights when the contribution is high with the assistance of attention weight modelling. As a result, the FT-CID server not only aggregates the local parameters but also considers the weights of the local learning model to accelerate global model convergence. The central server receives local model updates from 'n' different edges through TL, and it starts to retrain its predefined model based on the significance of the local models. For instance, the local models of different edges are denoted as LM= [CNN1], the corresponding weights are denoted as WLM=[W1], and the global model on the centralised server is modelled using Equation (2) across 'M' number of IIoT networks. \[W_{\text{GM}}=\sum_{\text{E}|\text{LM}=1}^{\text{M}}[|\text{N}|_{\text{LM}}* \text{h}_{\text{E}}(\text{i}-1)*W_{\text{LM}}] \tag{2}\]  
where 'N' refers to the total number of samples in each local learning model or IIoT network, and \(W_{\text{GM}}\) refers to the weight of the global learning model. In the FT-CID model, the weight of the global model relies on the weight of the local learning model, which is determined by the contribution level of each local model to global level optimisation through attention weight modelling. By applying Equation (3), the FT-CID model measures the influence of each local learning model to the global learning model. \[\text{d}(W_{\text{GM}}(\text{i}),W_{\text{LM}}(\text{i}))=\sqrt{\sum_{\text{i =1}}^{\text{n}}((W_{\text{GM}})_{\text{i}}-(W_{\text{LM}})_{\text{i}})^{2}} \tag{3}\]where \(\mathrm{d}(W_{\mathrm{GM}},W_{\mathrm{LM}})\) refers to the difference between the weights of the global shared model and each local model during the ith iteration, where 'i' ranges from 1 to n. Furthermore, 'd' is used to estimate the contribution of the local model parameters of each sensor to global model optimisation.