Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen, Dinh C.; Ding, Ming; Pathirana, Pubudu N.; Seneviratne, Aruna; Li, Jun; Poor, H. Vincent

doi:10.48550/arxiv.2104.07914

Cited by 8 publications

(10 citation statements)

References 82 publications

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“…Several researchers have separately presented engaging surveys on FL for vehicular networks [16,[18][19][20][21] and blockchain for vehicular networks [22][23][24][25][26]. However, to the best of our knowledge, this is the first time that blockchain and FL have been combined for vehicle networks.…”

Section: Comparison and Contributionmentioning

confidence: 99%

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

Javed

Hassan

Shahzad

et al. 2022

Sensors

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The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

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Section: Comparison and Contributionmentioning

confidence: 99%

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

Javed

Hassan

Shahzad

et al. 2022

Sensors

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“…The article first provides detailed analysis of privacy attacks on machine learning models and afterwards provide discussion about protection strategies that can be employed. From the perspective of Internet of Things (IoT) networks, machine learning, and privacy, a few research works [14]- [17] have been published in the recent past. The first work covering the use of machine learning in the security and privacy of IoT networks have been presented by Hussain et al in [14].…”

Section: A Comparison With Related Survey Articlesmentioning

confidence: 99%

“…A very detailed work showing the use cases and application of federated learning in IoT networks has been published by Khan et al in [16]. Similarly, another significant work covering the similar domain of federated learning and services and applications of IoT have been presented by authors in [17]. Alongside this, a thorough work discussing the overview of adversarial attacks alongside defence mechanisms in autonomous and interconnected vehicles have been presented by Qayyum et al in [6].…”

Section: A Comparison With Related Survey Articlesmentioning

confidence: 99%

Privacy Preserving Machine Learning for Electric Vehicles: A Survey

Sani¹,

Hassan²,

Chen³

2022

Preprint

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In the recent years, the interest of individual users in modern electric vehicles (EVs) has grown exponentially. An EV has two major components, which make it different from traditional vehicles, first is its environment friendly nature because of being electric, and second is the interconnection ability of these vehicles because of modern information and communication technologies (ICTs). Both of these features are playing a key role in the development of EVs, and both academia and industry personals are working towards development of modern protocols for EV networks. All these interactions, whether from energy perspective or from communication perspective, both are generating a tremendous amount of data every day. In order to get most out of this data collected from EVs, research works have highlighted the use of machine/deep learning techniques for various EV applications. This interaction is quite fruitful, but it also comes with a critical concern of privacy leakage during collection, storage, and training of vehicular data. Therefore, alongside developing machine/deep learning techniques for EVs, it is also critical to ensure that they are resilient to private information leakage and attacks. In this paper, we begin with the discussion about essential background on EVs and privacy preservation techniques, followed by a brief overview of privacy preservation in EVs using machine learning techniques. Particularly, we also focus on an in-depth review of the integration of privacy techniques in EVs and highlighted different application scenarios in EVs. Alongside this, we provide a a very detailed survey of current works on privacy preserving machine/deep learning techniques used for modern EVs. Finally, we present the certain research issues, critical challenges, and future directions of research for researchers working in privacy preservation in EVs.

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“…Though a few survey papers on FL [1], [2], [3], [4], [5], [6], [7] have been published, to the best of our knowledge, there are currently no relevant survey papers focused on FRL. Due to the fact that FRL is a relatively new technique, most researchers may be unfamiliar with it to some extent.…”

Section: Introductionmentioning

confidence: 99%

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Qi¹,

Zhou²,

Lei³

et al. 2021

Preprint

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This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e., Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.

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Federated Learning for Internet of Things: A Comprehensive Survey

Cited by 8 publications

References 82 publications

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

Privacy Preserving Machine Learning for Electric Vehicles: A Survey

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

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