## 5 Discussion  
In this section, the proposed model is compared with several frameworks that were implemented on CICIoT2023. In this study, we implemented FEDXAIIDS as the baseline model on CICIoT2023, and our comparison study focused on the performance of different frameworks on the CICIoT2023 data set. The findings in Table 2 show that FedXAIIDS is effective in addressing major intrusion detection concerns, particularly in terms of privacy preservation, explainability, and decentralized learning. While the recommended framework attained a training efficacy of 88.4% and a testing efficacy of 88.2% on the CICIoT2023 data set, which are lower than some centralized approaches, its advantages in security, interpretability, and real-world applicability make it a superior choice for modern cyber security frameworks. FedXAIIDS, unlike standard IDS models, uses federated learning (FL) to ensure that sensitive network data are dispersed across edge devices. This considerably decreases the danger of data breaches and ensures compliance with severe privacy requirements such as the GDPR (General Data Protection Regulation). In an article [44], the proposed framework achieved impressive efficacy of 100% for sql injection attacks and a 98.25% success rate for brute-force attacks, but the limitation of this work was that it only focused on identifying two attacks. The majority of works have prioritized evaluating accuracy, ignoring the need for a justification of the gained accuracy, while FEDXAIIDS properly justifies the achieved accuracy by evaluating feature contribution utilizing SHAP. Despite yielding high accuracy, 99.41% for multi-attack and 99.83% for binary classification, the proposed model of the article [45] is computationally expensive for using multiple techniques in a single architecture. Similarly, ref. [46] implies a 1D to 3D synaptic transformation on a large data set, CICIoT2023, to achieve 97.69% accuracy while increasing the computational overhead. In the article [47], the authors experimented and autoencoder-based feedforward neural network with the success rate of 99.55%, but it is a centralized system that still involves a privacy issue. [PERSON] et al. [48] proposed a CPS-based framework integrating Adaboost and RF, achieving 98.27% accuracy while adopting a centralized method that does not protect the confidentiality of end-to-end devices. On the other hand, [PERSON] et el. [24] overcame the privacy issue by applying Diffie-Hellman key exchange and homomorphic encryption, achieving 97.31% accuracy while maintaining unclarity about the number of attack types it is identifying. The most competitive article with our work is the article [25], with the framework of FDNN with XAI having a success rate of 99.78%, but it only focused on classifying different types of DDoS attacks, while [PERSON] et al. proposed NIDS-FGPA with Pailier encryption to resolve security issues, having a success rate of 99.2% on CICIoT2023 but computational complexity [1]. Additionally, the primary objective of the current experiment was to ensure interpretability by calculating SHAP values, but our future research might look at utilizing top-ranked features that SHAP discovered for dimensionality reduction. This framework might even increase or sustain performance while supporting the development of a more computationally efficient infrastructure. From the above discussion, we can observe that our proposed framework performed worse in accuracy than the other compared frameworks, which is a vulnerable point in the proposed study. But the proposed infrastructure is computationally lower in cost, as we opted for an ANN as a local framework in an FL environment. Moreover, the significant factor about our proposed infrastructure, FEDXAIIDS, is that our model has generated a set of significant features implementing XAI and particularly showed their contribution to the detection of different attack classes. According to the SHAP analysis plot, we have observed that the feature 28-UDP has the most significance in detecting any attack class.