FLIoDT:从设计隐私到物联网默认隐私的联邦学习架构

Feras M. Awaysheh, Sadi Alawadi, Sawsan Al-Zubi
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引用次数: 2

摘要

物联网(IoT)实现了具有良好功能的智能设备的指数级增长,预示着边缘智能时代的到来。这种模式适时地需要将许多计算转移到更靠近网络边缘数据源的位置。数据隐私至关重要,因为安全漏洞可能会严重影响这样一个具有巨大攻击面的环境。联邦学习(FL)是一种基于分散机器学习(ML)的隐私设计,它的出现使参与者能够在不共享敏感数据的情况下协作训练模型。然而,隐私影响是一个明显的问题,也是扩大FL方法的使用及其在物联网应用中的大规模采用的障碍。本文介绍了基于断开连接物联网(flodt)的FL概念,这是继气隙网络之后的一种功能分离。FLIoDT提供了一种实用的方法来减轻FL领域的数据威胁/攻击。FLIoDT证明了一种实用的架构方法,可以减轻Edge环境中的几种攻击。数据挖掘和对抗性攻击,比如数据中毒。本研究通过边缘计算研究健康监测患者数据的人类活动识别,以验证FLIoDT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FLIoDT: A Federated Learning Architecture from Privacy by Design to Privacy by Default over IoT
The Internet of Things (IoT) realized exponential growth of smart devices with decent capabilities, promising an era of Edge Intelligence. This paradigm creates a timely need to shift many computations closer to the data source at the network's edge. Data privacy is paramount, as security breaches can severely impact such an environment with its vast attack surface. The advent of Federated learning (FL), a privacy-by-design with decentralized machine learning (ML), enables participants to collaboratively train a model without sharing their sensitive data. Nevertheless, privacy implications are a glaring concern and perrier for widening the utilization of FL approaches and their mass adoption over IoT applications. This paper introduces the notion of FL over the Internet of Disconnected Things (FLIoDT), a functionality separation of concerns following the air-gapped networks. FLIoDT provides a practical methodology to mitigate Data threats/attacks in the FL domain. FLIoDT proves a practical architectural approach to mitigate several attacks in an Edge environment. Data dredging and adversarial attacks, like data poisoning, to name some. This study investigates human activity recognition of health monitoring patient data over edge computing to validate FLIoDT.
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