## 6 Conclusions  
This research introduced Federated XAI IDS, a novel explainable and privacy-preserving intrusion detection system (IDS) that effectively addresses the key limitations of traditional IDS, including high inaccurate positive detection and inaccurate negative detection percentage, a lack of interpretability, and data privacy concerns. By leveraging federated learning (FL) and Shapley additive explanations (SHAPs), our approach ensures that IDS models can be collaboratively trained across multiple decentralized devices while preserving data privacy by keeping sensitive information on local edge nodes. This decentralized paradigm mitigates the security risks associated with centralized approaches, making it a commendatory solution for modern network circumstances. The proposed IDS framework utilizes an artificial neural network (ANN) distributed across four federated clients, with model aggregation performed using FedAvg on the CICIoT2023 data set. The output highlighted the efficiency of this approach, achieving 88.4% training accuracy and 88.2% testing accuracy. Additionally, SHAP was incorporated to analyze feature importance, providing a transparent perspective of the most significant attributes influencing model predictions. The ability to rank and interpret feature significance enhances model trustworthiness and supports cyber security professionals in making informed decisions. Though our accuracy is lower than that of other current studies, SHAP analysis ensured the efficiency our results, and our findings demonstrate that Federated XAI IDS successfully tackles two critical challenges in intrusion detection, explainability, and privacy preservation. By integrating federated learning with explainable AI (XAI), this work offers a scalable, interpretable, and secure IDS solution suited for modern cyber security applications, particularly in scenarios where sensitive data cannot be centrally shared. Moreover, it was found in the proposed method that the most significant feature in contributing to detect intrusion is UDP. This information can pave the way to build an efficient intrusion prevention system to protect the system from any kind of breaching in the first place. For a future extension of this work, we will experiment with different federated aggregation tactics, as well as XAI methods, to analyze the performance efficiency shift and significant features. Additionally, we would like to implement the framework on a real-life network system to test its eligibility to work in an actual network layer as a realistic implementation, and an assessment in extensive and diverse networks will confirm the resilience and applicability of our methodology. Conceptualization, K.F. and R.M. ; Methodology, K.F. and S.K.D. ; Validation, S.K.D. and C.S. ; Formal analysis, S.K.D. ; Investigation, K.F., M.A., and R.T.K. ; Resources, M.A. and R.T.K. ; Data curation, M.A. and M.M.R. ; Writing--original draft, K.F. ; Writing--review & editing, M.M.R., C.S. and R.M. ; Supervision, R.M. and S.K.D. All authors have read and agreed to the published version of the manuscript. This research work is partially funded by the University Grants Commission (UGC) of Bangladesh with Grant number: 37.01.0000.073.12.009.23.458. The data that support the findings of this study are openly available in the University of New Brunswick Canadian Institute for Cybersecurity (CIC) at [[https://www.unb.ca/cic/datasets/iotdataset-2023.html](https://www.unb.ca/cic/datasets/iotdataset-2023.html)]([https://www.unb.ca/cic/datasets/iotdataset-2023.html](https://www.unb.ca/cic/datasets/iotdataset-2023.html)) accessed on 20 May 2025. This study is the extended version of our earlier work that was published at a conference [8]. Additionally, in the writing of this publication, we employed AI-assisted technologies, namely OpenAI's ChatGPT-4, to facilitate language refining, paraphrasing, section structuring, and a summary of background literature.