One of the major bottlenecks for deploying popular first-order differentially private (DP) machine learning algorithms (e.g., DP-SGD) lies in their high computation and memory cost, despite the existence of optimized implementations. Zeroth-order methods have promise in mitigating the overhead, as they leverage function evaluations to approximate the gradients, hence significantly easier to privatize. While recent works have explored zeroth-order approaches in both private and non-private settings, they still suffer from relatively low utilities compared with DP-SGD, and have only been evaluated in limited application domains. In this work, we propose to leverage public information to guide and improve gradient approximation of private zeroth-order algorithms. We explore a suite of \underline{p}ublic-data-\underline{a}ssisted \underline{z}eroth-\underline{o}rder optimizers (PAZO) with minimal overhead. We provide theoretical analyses of the PAZO framework under an assumption of the similarity between public and private data. Empirically, we demonstrate that PAZO achieves superior privacy/utility tradeoffs across vision and text tasks in both pre-training and fine-tuning settings, outperforming the best first-order baselines (with public data) especially in highly private regimes, while offering up to $16\times$ runtime speedup.

Given that very few action recognition datasets collected in elevators contain multimodal data, we collect and propose our multimodal dataset investigating passenger safety and inappropriate elevator usage. Moreover, we present a novel framework (RGBP) to utilize multimodal data to enhance unimodal test performance for the task of abnormal event recognition in elevators. Experimental results show that the best network architecture with the RGBP framework effectively improves the unimodal inference performance on the Elevator RGBD dataset by 4.71% (accuracy) and 4.95% (F1 score) with respect to the pure RGB model. In addition, our RGBP framework outperforms two other methods for ”multimodal training and unimodal inference”: MTUT [1] and the two-stage method based on depth estimation.