Nilay Khare

Hierarchical federated edge learning with criticality-aware aggregation for privacy-preserving fetal health classification

The automated classification of fetal health from cardiotocographic (CTG) recordings is a crucial machine learning application in obstetric diagnostics. Although centralized predictive models have advanced significantly, their large-scale clinical deployment remains structurally constrained by institutional data privacy regulations, data silos, and significant distributional heterogeneity across different recording equipment. To address these interconnected challenges, this study introduces a novel Criticality-Aware Federated Learning framework with Hierarchical Edge Aggregation, designated as FedCrit-HEA. The primary architectural novelty of this framework lies in its two-tier hierarchical federated topology combined with an adaptive optimization layer. Hospital clients optimize local parameters independently and transmit differentially private gradients to regional edge servers for preliminary aggregation, which systematically isolates wide-area network bottlenecks. Crucially, the framework introduces a novel criticality-aware global aggregation mechanism that dynamically adjusts client parameter contributions based on a pathological-sample density metric derived from localized class-distribution statistics. This targeted operator inherently prevents the suppression of clinically critical minority-class gradient information by normal-class dominant clients, which is a persistent limitation in standard federated averaging. Validated across two distinct clinical repositories—the UCI Cardiotocography and the CTU-CHB Dataset—the framework achieves a global macro-averaged F1-score of 0.904 and a pathological-class recall of 84.2% on the UCI partition, alongside an F1-score of 0.901 and a pathological recall of 85.6% on the CTU-CHB data. Simultaneously, the hierarchical edge-aggregation layout reduces wide-area network communication overhead by up to 75% compared to conventional flat federated configurations. These outcomes demonstrate the feasibility and architectural robust novelty of collaborative, privacy-preserving, and minority-sensitive diagnostic modeling within distributed healthcare ecosystems.

Flip-med: federated learning for innovative precision medicine - enhancing data privacy and predictive performance

EQS-net for feature-optimized cervical cancer diagnosis using multi-layer fusion & dimensionality reduction

Cervical cancer remains a leading cause of cancer-related mortality among women worldwide, yet its progression is largely preventable through timely and accurate diagnosis. Conventional Pap smear screening pipelines, however, are constrained by subjective interpretation, diagnostic complexity, and limited throughput — barriers that impede scalable deployment in resource-limited settings. To address these critical gaps, this study presents EQS-NET, a novel, lightweight, and feature-optimized Computer-Aided Diagnosis (CAD) framework that leverages a heterogeneous ensemble of compact CNNs — ShuffleNet, SqueezeNet, and EfficientNet — to extract complementary deep representations via transfer learning. EQS-NET employs a Multi-Layer Deep Feature Fusion strategy aggregating discriminative features across the final three convolutional layers of each network, refined through mRMR-based feature selection. The framework eliminates the need for hand-crafted feature engineering, image segmentation, and cytology-specific preprocessing, relying only on minimal standard preprocessing. Validated under stratified 5-fold cross-validation on two benchmark datasets, EQS-NET achieves 98.5% ± 0.18% on SIPaKMeD and 99.98% ± 0.04% on Mendeley LBC (p < 0.001 against single-CNN baselines), outperforming existing state-of-the-art methods across sensitivity, specificity, and AUC — establishing it as a scalable, efficient, and clinically viable solution for automated cervical cancer screening.