Imagine running massive AI models, like those powering image recognition or chatbots, right on your phone. The problem? These models are enormous, demanding hefty processing power and memory, making them unsuitable for mobile devices. New research tackles this challenge by introducing a 'personalized compression' algorithm. The core idea is to trim the fat from pre-trained AI models, discarding unnecessary parts while retaining the essential knowledge for specific user data. Traditionally, models are trained on vast, general datasets. This research takes a different approach. It identifies and preserves the model components most relevant to *personalized* data, like the photos on your phone or your specific conversation style, creating a leaner model tailored just for you. The method borrows from 'compressed sensing,' a technique that efficiently captures sparse signals. It randomly samples parts of the model, identifying the most important pieces for reconstructing personalized results. This process distinguishes between 'personalized layers'— crucial for individual data—and 'generic layers'—important for general tasks but less vital for personalized results. By applying different compression levels to these layers, the algorithm drastically reduces the model's size while preserving accuracy on personalized tasks. Experiments show this approach significantly shrinks large vision and language models while maintaining impressive performance on personalized data. This opens up possibilities for running powerful AI directly on mobile devices, paving the way for faster, more efficient, and private AI experiences.