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<p>Due to privacy concerns, processing resources, and platform execution characteristics, distributed systems with high-dimensional datasets make feature selection problematic. Traditional techniques prioritize statistical relevance above structural manifold consistency, federated aggregation stability, and cross-platform runtime Variability In Process. The Federated Iterative Entropy Validated Optimization Framework, an integrated solution for compact and structurally coherent feature sets in decentralized analytics pipelines, tackles these difficulties. The framework finds discriminative features and reduces redundancy using entropy-weighted optimization. Class-level geometric separability is maintained by structurally validating retained features with spectral embedding. Probability-based scoring allows nodes to contribute relevance measures without raw data samples by federating local selections. This ensures anonymity and stands up to uneven data distribution. Several distributed processing frameworks use a cross-platform execution engine to analyze feature subsets for latency, memory behavior, and computational utilization. In an iterative corrective loop, execution feedback modifies entropy weights for platform-level process performance anomalies. Experimentally evaluated benchmark datasets CICIDS2017, UNSW-NB15, and a synthetic dataset of ten million samples. The recommended framework reduces feature space by 86–89% and improves classification accuracy to 93–95%. A steady increase in F1-scores indicates balanced precision and recall. Runtime efficiency improves 30–40% across studied systems, while distributed engine execution variability stays below 12%. This method converges in 11–13 iterations, reducing processing overhead compared to 18–22 for comparable algorithms. We found that entropy-driven selection, spectral validation, federated probability consensus, and cross-platform feedback scale privacy-aware feature optimization. In modern decentralized analytics systems, these components balance dimensionality reduction, predictive performance, and resource utilization. The application of node reliability modeling to unstructured modalities may help extremely dynamic deployments.</p>

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Design of an improved validation model using entropy-weighted optimizations and Laplacian spectral embedding for federated feature selection operations

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Abhimanyu Dutonde,
  • Shrikant Sonekar

摘要

Due to privacy concerns, processing resources, and platform execution characteristics, distributed systems with high-dimensional datasets make feature selection problematic. Traditional techniques prioritize statistical relevance above structural manifold consistency, federated aggregation stability, and cross-platform runtime Variability In Process. The Federated Iterative Entropy Validated Optimization Framework, an integrated solution for compact and structurally coherent feature sets in decentralized analytics pipelines, tackles these difficulties. The framework finds discriminative features and reduces redundancy using entropy-weighted optimization. Class-level geometric separability is maintained by structurally validating retained features with spectral embedding. Probability-based scoring allows nodes to contribute relevance measures without raw data samples by federating local selections. This ensures anonymity and stands up to uneven data distribution. Several distributed processing frameworks use a cross-platform execution engine to analyze feature subsets for latency, memory behavior, and computational utilization. In an iterative corrective loop, execution feedback modifies entropy weights for platform-level process performance anomalies. Experimentally evaluated benchmark datasets CICIDS2017, UNSW-NB15, and a synthetic dataset of ten million samples. The recommended framework reduces feature space by 86–89% and improves classification accuracy to 93–95%. A steady increase in F1-scores indicates balanced precision and recall. Runtime efficiency improves 30–40% across studied systems, while distributed engine execution variability stays below 12%. This method converges in 11–13 iterations, reducing processing overhead compared to 18–22 for comparable algorithms. We found that entropy-driven selection, spectral validation, federated probability consensus, and cross-platform feedback scale privacy-aware feature optimization. In modern decentralized analytics systems, these components balance dimensionality reduction, predictive performance, and resource utilization. The application of node reliability modeling to unstructured modalities may help extremely dynamic deployments.