Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities

Future Internet Pub Date : 2024-02-28 DOI:10.3390/fi16030082
Hanyue Xu, K. Seng, Jeremy Smith, L. Ang
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Abstract

In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy.
基于智慧城市的大规模人工智能物联网系统的多级拆分联合学习
在智慧城市的背景下,人工智能(AI)与物联网(IoT)的融合导致了 AIoT 系统的激增,这些系统可处理大量数据,以增强城市基础设施和服务。然而,在这些系统中协同训练深度学习模型遇到了重大挑战,主要是由于数据隐私问题和处理来自大规模物联网设备的通信延迟。为了解决这些问题,有人提出了多级分离式联合学习(multi-level split federated learning,简称SFL),它融合了分离式学习(SL)和联合学习(FL)的优点。该框架引入了一种新颖的多级聚合架构,可减少通信延迟,提高可扩展性,并解决具有非 IID 数据分布的大型人工智能物联网系统固有的系统和统计异质性问题。该架构利用消息队列遥测传输(MQTT)协议对物联网设备进行地理集群,并采用边缘和雾计算层进行初始模型参数聚合。仿真实验验证了多层次 SFL 的性能优于传统 SFL,在大规模非 IID 环境中提高了模型精度和收敛速度。本文阐述了所提出的架构、工作流程及其在提高智慧城市中人工智能物联网系统的鲁棒性和可扩展性方面的优势,同时保护了数据隐私。
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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