I am researching how to enhance anomaly detection in edge computing using deep learning. The main challenge is the limited computational resources available on edge devices.

I am considering a hybrid model that combines lightweight neural networks with traditional anomaly detection algorithms. Are there any recent studies or research papers on this topic?

Additionally, what techniques or tools could help optimize deep learning models for edge computing? Any recommendations for frameworks or efficient architectures would be highly appreciated!