PPTADI: A privacy-preserving training and accelerated distributed inference framework in low-resource AIoT scenarios

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-16 DOI:10.1016/j.future.2025.108084

Haoyang Meng, Yizhong Hu, Kexian Liu, Jianfeng Guan

引用次数: 0

Abstract

Artificial intelligence of things (AIoT) is a technology that combines AI and IoT, which realizes the interconnection and gives them more intelligent features between devices. However, there are some challenges in the data process. In this paper, we propose a new framework called PPTADI to train AI models while preserving privacy and to accelerate the inference process. Experiments show that PPTADI can effectively prevent label leakage, gradient attacks and model inversion attacks compared to the conventional split federated learning frameworks. In the meanwhile, PPTADI reduces the total inference delay by up to 35 % and the transmission delay by up to 65 % comparing with some SOTA schemes for distributed inference.

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PPTADI：低资源AIoT场景下的隐私保护训练和加速分布式推理框架

物联网人工智能（AIoT）是一种将AI和IoT结合起来的技术，实现了设备之间的互联互通，赋予它们更多的智能特性。然而，在数据处理方面存在一些挑战。在本文中，我们提出了一个名为PPTADI的新框架来训练人工智能模型，同时保护隐私并加速推理过程。实验表明，与传统的分裂联邦学习框架相比，PPTADI可以有效地防止标签泄漏、梯度攻击和模型反转攻击。同时，与一些SOTA分布式推理方案相比，PPTADI可将总推理延迟降低35%，传输延迟降低65%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.