This paper presents a comprehensive comparison of XAI (Explainable AI) techniques applied to analyze the influence of experts and features in a multi-expert feature selection system for intrusion detection systems deployed on edge computing platforms. The study focuses on four prominent XAI methods — SHAP, LIME, Integrated Gradients, and DeepLIFT — to evaluate their effectiveness in explaining feature importance and the contribution of component experts in the decision-making process of the multi-expert system. The comparison is based on quantitative metrics: Hamming Distance, Spearman Rank, Pearson Correlation, Jaccard Index, and Cosine Similarity, which help analyze the level of consistency and differences between explanations or selected feature sets. The experimental results provide deep insights into the advantages and limitations of each method, thereby suggesting the selection of appropriate explanation techniques that meet the specific requirements of machine learning-based intrusion detection systems in edge computing environments.