IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning

Aïvodji, Ulrich; Gambs, Sébastien; Martin, Alexandre

doi:10.1109/spw.2019.00041

Cited by 60 publications

(25 citation statements)

References 15 publications

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“…3) FL Implementation and Testbeds in industrial IoT: Inspired by the great potential of FL in IoT systems, there are several recent projects implemented to investigate the feasibility of FL in real-life industrial applications. As an example, the work in [182] implements a testbed for an FL-based smart home platform in a real-world IoT setting. More specifically, a smart home architecture is proposed, consisting of smart home IoT devices (e.g., camera, light bulb, door locks), a router, and an intrusion detection system with a SQLite database.…”

Section: Model M2mentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

693

182

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Section: Model M2mentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

693

182

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“…Therefore, FL aims to mitigate the above problems by training algorithms collaboratively without sharing the data themselves. By combining federated learning and data aggregation with a strong focus on security and privacy, the authors of [219] proposed IOTFLA, an architecture for smart homes. Furthermore, FL can be used to solve privacy issues and reduce the risk of data breaches for clinical information as it does not require data transmission and centralization.…”

Section: Federated Learning In Edge Computing: Case Studiesmentioning

confidence: 99%

Federated Learning in Edge Computing: A Systematic Survey

Abreha

Hayajneh

Serhani

2022

Sensors

158

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Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

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“…To ensure the anonymity and privacy of IoT data in edge devices, the authors of [57] proposed a blockchain based on a decentralized FL architecture. The security and privacy of IoHT data used within the data aggregation cycle of FL has been studied by the authors of [58] in a smart home context.…”

Section: Literature Reviewmentioning

confidence: 99%

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

et al. 2020

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Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

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IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning

Cited by 60 publications

References 15 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning in Edge Computing: A Systematic Survey

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

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