How does the communication overhead of gradient updates in adaptive federated LoRA scale with model size compared to

Abstract

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional

Research Question

How does the communication overhead of gradient updates in adaptive federated LoRA scale with model size compared to full-parameter fine-tuning when evaluated on heterogeneous edge device simulations?

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