{"title":"Privacy-preserving edge federated learning for intelligent mobile-health systems","authors":"","doi":"10.1016/j.future.2024.07.035","DOIUrl":null,"url":null,"abstract":"<div><p>Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients’ mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon’s AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24003972/pdfft?md5=967d2951f36ac1c92466c6a4ee5f41a2&pid=1-s2.0-S0167739X24003972-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24003972","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients’ mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon’s AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies.
机器学习(ML)算法通常是针对所有数据都存储在一个数据中心并在那里进行训练的场景而设计的。然而,在许多应用中,例如在医疗保健领域,训练数据分布在多个实体中,例如不同的医院或患者的移动设备/传感器。同时,由于隐私和法律问题,以及某些情况下的通信和计算开销,将数据传输到一个中心位置进行学习肯定不是一种选择。联合学习(FL)是最先进的协作式 ML 方法,用于在多方持有本地数据样本的情况下训练 ML 模型,而无需共享这些样本。然而,在这种边缘物联网(IoT)系统(如移动健康和可穿戴技术,涉及敏感的个人/医疗数据)上以保护隐私的方式从分布式数据中进行学习是一项重大挑战,这主要是由于其严格的资源限制,即有限的计算能力、通信带宽、内存存储和电池寿命。在本文中,我们针对物联网基础设施上资源受限的移动健康和可穿戴技术提出了一种保护隐私的边缘 FL 框架。我们对所提出的框架进行了广泛评估,并基于使用可穿戴技术的癫痫监测中的癫痫发作检测应用,在亚马逊的 AWS 云平台上实现了我们的技术。
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.