Foundation Model Distillation Techniques for Resource-Efficient Predictive Analytics

Abstract

Foundation models have significantly improved predictive analytics across healthcare, finance, manufacturing, cybersecurity, transportation, retail, and scientific research by enabling accurate forecasting, classification, anomaly detection, and intelligent decision support. However, their large computational requirements, memory consumption, energy usage, and inference latency limit deployment on resource-constrained platforms such as edge devices, IoT systems, mobile devices, and embedded applications. Knowledge distillation has emerged as an effective approach for developing resource-efficient AI by transferring knowledge from large teacher models to compact student models while preserving predictive performance. Unlike conventional compression techniques, it retains semantic representations and improves model efficiency through advanced strategies such as feature distillation, self-distillation, multi-teacher learning, and adaptive optimization. This paper proposes a Foundation Model Distillation Framework (FMDF) for predictive analytics in heterogeneous computing environments. The framework integrates teacher–student learning, adaptive feature distillation, multi-level knowledge transfer, dynamic loss optimization, and resource-aware inference to enable efficient deployment across cloud, edge, mobile, and embedded platforms. The proposed methodology includes foundation model training, hierarchical knowledge distillation, lightweight model optimization, and continuous performance evaluation. Mathematical formulations support knowledge transfer, prediction optimization, computational efficiency, and model compression. Experimental results demonstrate that FMDF significantly reduces model complexity while maintaining high predictive accuracy, scalability, robustness, and energy efficiency, making it a promising solution for resource-efficient predictive AI, edge intelligence, federated learning, digital twins, and next-generation intelligent decision support systems.