xApp Distillation

xApp distillation is a technique for consolidating the knowledge from multiple machine learning models (xApps) into a single, more efficient model, primarily addressing conflicts arising from their overlapping deployments in areas like telecommunications networks or medical data analysis. Current research focuses on improving the cross-architecture generalization of distilled models, enhancing their interpretability through methods like image highlighting and decision tree distillation, and employing techniques such as contrastive learning and f-divergence minimization to optimize knowledge transfer. This approach offers significant potential for improving resource efficiency, mitigating conflicts in complex systems, and enhancing the explainability and usability of sophisticated machine learning models across diverse applications.