## 1 Introduction
Research, including [9], demonstrates that SHAP provides equitable and consistent feature importance ratings, making it an ideal method for enhancing the interpretability of IDS. Moreover, SHAP facilitates feature selection and optimization, hence enhancing generalization and efficiency in IDSs that are constructed deploying FL, where privacy preservation is paramount [10]. Utilizing SHAP in FL-based IDS confidentiality security insights while safeguarding raw data, hence maintaining user privacy and delivering actionable knowledge for cyber defense. The CICIoT2023 data set [11], a real-world data set with contemporary attack types, has been used for experiments. The system's performance and generalizability in a range of intrusion scenarios, as well as its capacity to identify known and new network threats, have been the main areas of focus. In addition, the integration of explainable artificial intelligence (XAI) techniques, particularly SHAP, will provide essential transparency within the IDS framework. This comprehensability constitutes an essential towards instilling assurance in AI-driven frameworks, specifically when legal and regulatory compliance is a factor in the deployment of network security solutions. In this study, an artificial neural network (ANN) is incorporated as the local framework, as it is capable of adopting complicated, non-linear relationships within the high dimensional feature that is the nature of network traffic. Other ML frameworks such as support vector machines (SVMs), decision trees (DTs), and convolutional neural networks (CNNs) have been incorporated into IDS. However, several studies have demonstrated comparatively higher success rate in various IDS benchmarks [12]. On the other hand, SVM model can be impressively effective on high-dimensional spaces but can produce high computational costs for larger data sets, and it can struggle with highly complicated patterns compared to an ANN [13]. Though decision trees are interpretable frameworks, they can be vulnerable to overfitting and struggle with complicated trends like ANNs [14]. Furthermore, CNN models excel in handling spatial data, and they have been incorporated into IDS for transforming network traffic into image representations. However, ANNs, specifically RNNs and their classifications and feedforward neural networks, can perform more effectively than CNNs [15]. Moreover, we chose to incorporate a basic ANN into our proposed FEDXAIIDS to balance the computational complexity of the framework with training efficiency in a distributed system. Additionally, we incorporated the CICIoT2023 data set, as it provides large coverage on attack classes extracted from a real-life IoT scenario [11]. This data set is more updated than the earlier edition of the IDS data set, CICIDS2017 and CICIDS2018, which did not encounter particular IoT attacks that may have represented the IoT network more accurately [16]. On the other hand, ToN-IoT did not cover the same margin of the device to enlist the attacks [17]. Thus, we came to the decision to use CICIoT2023 for our proposed architecture. The new study builds on the foundations laid by focusing on explainable AI and federated learning, while the earlier work concentrated on these conventional approaches. The goal is to improve the privacy, efficacy, and interpretability of IDS solutions. This research builds upon prior work with the CIC_IDS_2018 data set, where the challenge of imbalanced data sets was addressed by developing an IDS using balanced data and exploring unorthodox categorization methods incorporating the Dendritic Cell Algorithm (DCA) [18]. In order to improve transparency and interpretability and facilitate a more thorough comprehension of model decisions, explainable artificial intelligence (XAI) approaches can be incorporated into the IDS framework. By elucidating the reasoning behind certain results, this transparency contributes to the development of trust in AI-driven frameworks, which is crucial for fulfilling legal and regulatory obligations.