Research Archive

Comprehensive implementation and demonstration of federated multi-view clustering with rectified Gaussian kernels. Features privacy-preserving distributed learning across healthcare institutions with 32.7% performance improvement over local models. Includes complete algorithm implementation, 12+ visualizations, and publication-ready results.

Comprehensive implementation of Federated Multi-View K-Means Clustering with Rectified Gaussian Kernel. Features privacy-preserving distributed learning across multiple sites with excellent performance metrics. The implementation shows a 32.7% improvement over local models with privacy level of 0.9.

Comprehensive tutorial demonstrating federated learning implementation using the Flower framework on MNIST data. Features client-server architecture, FedAvg strategy, and performance visualization across multiple federated clients. Includes practical code examples, training loops, and scalability demonstrations with up to 200+ clients.

Design principles and implementation strategies for building scalable, modular multi-view clustering frameworks. Covers dynamic feature integration, interpretability mechanisms, and performance tracking across federated environments.

Development of lightweight anomaly detection models optimized for industrial edge devices. Focuses on minimal communication costs and privacy preservation across simulated sensor networks.

Interactive visualization techniques for understanding peer-to-peer learning dynamics across edge devices. Features real-time traffic simulation data and dynamic graph overlays for federated network analysis.

Privacy-preserving machine learning across distributed networks, federated optimization strategies, and collaborative learning without centralized data sharing.

Techniques for integrating multiple data perspectives, view weight learning, and enhanced distance metrics for multi-modal data analysis.

Differential privacy mechanisms, secure multi-party computation, and privacy-aware machine learning for sensitive healthcare applications.

Practical guidelines for implementing research algorithms with focus on reproducibility, modularity, and performance optimization.

Curated collection of visualization techniques, plotting libraries, and interactive tools for presenting machine learning research results.