## 1 Introduction  
In the digital age, cyber security--the practice of safeguarding frameworks, networks, and confidential data from unwelcome inspection, distress, and emerging cyber hazards [1]--has become an essential battle line. Since people, organizations, and governments grow more interconnected and dependent on digital infrastructure, it is crucial to guarantee data availability, confidentiality, and integrity. In order to safeguard their valuable assets and lower risks, companies need to proactively create multilayer protection measures in response to evolving cyber threats. Within the ever-changing field of cyber security, safeguarding private data from a continuously increasing spectrum of cyber attacks is essential. In order to discover and prevent undesirable activity in network traffic, intrusion detection systems (IDS) are essential. They are the first line of defense against malicious attacks. An intrusion detection system (IDS) scans over malicious activity or unauthorized access through analysis of traffic dynamics, applications and session behavior, and signature-based features [2]. By emphasizing anomalies and recognized attack patterns, IDS alerts help organizations promptly address possible security risks. In this new age of interconnected world, approximately 50% of startups disclose experiencing information theft, underscoring the urgency of robust IDS solutions in institutional security frameworks [3]. The integration of deep learning (DL) and machine learning (ML) along with IDS has acquired substantial traction due to their superior classification accuracy. Nonetheless, conventional machine learning-based intrusion detection systems encounter considerable privacy and security vulnerabilities, owing to their dependence on centralized data storage and transmission [4]. Additionally, The challenges of developing effective IDS systems include the substantial quantity of network information and the prevalence of imbalanced data sets, where minor yet critical attack types are often underrepresented [5]. The current advances in deep learning, particularly the use of the transformer technique, have demonstrated significant potential in identifying anomalies and interpreting the order for advanced performance in decoding complicated patterns [6], which significantly prevents complicated network intrusion in IDS. However, there are serious concerns regarding confidentiality, with the use of deep learning-based IDS frameworks, especially when models are trained using private network traffic data. These issues can be effectively addressed via federated learning (FL), which permits for decentralized coordinated infrastructure refinement while maintaining the privacy and security of confidential information [7]. This research introduces a federated learning-based intrusion detection system that employs artificial neural networks as the local-end prediction system and incorporates Shapley additive explanations in order to enhance comprehensibility. The decentralized training approach guarantees confidentiality while facilitating visibility in detecting threats to networks. This piece of literature is the expanded version of the initial manuscript previously laid out in [8]. Explainable artificial intelligence (XAI) has significantly enhanced intrusion detection systems (IDSs) by providing accountability, understanding, and integrity in decision-making. Traditional IDS models, particularly those based on deep learning, frequently function as \"black boxes\", complicating security analysts' understanding of their predictions. Shapley additive explanations (SHAPs) have emerged as a powerful XAI approach, offering feature attribution scores that elucidate the influence of different input characteristics on the conclusions of the models. Employing SHAP in intrusion detection systems aids researchers and practitioners in identifying significant attack patterns, reducing false positives, and enhancing model robustness.