How does adaptive LoRA rank allocation impact convergence rates and final accuracy on non-IID text classification benchmarks

Abstract

Abstract: With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet th

Research Question

How does adaptive LoRA rank allocation impact convergence rates and final accuracy on non-IID text classification benchmarks compared to static rank configurations in federated learning?

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