The FGFP framework introduces a novel method for compressing deep neural networks using fractional Gaussian filters and adaptive unstructured pruning. By minimizing the number of parameters and leveraging Grünwald-Letnikov fractional derivatives, it achieves significant model size reductions with minimal impact on accuracy, as demonstrated on benchmarks like CIFAR-10 and ImageNet2012.