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:10.1109/comst.2021.3075439

Cited by 603 publications

(256 citation statements)

References 160 publications

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“…After local training, all institutions transmit the learned updates to the cloud server for model averaging, while actual COVID-19 X-ray images are kept at local institutions which thus ensures data privacy. Then, the cloud server sends the new global update back to all participating institutions to prepare for the next round of FL training [154] . The learning process is iterated until the global loss function converges with a desired accuracy, where the synthetic COVID-19 X-ray images are generated with high-quality with respect to actual images.…”

Section: A Case Studymentioning

confidence: 99%

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

et al. 2021

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. Blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19 . Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronaviruslike epidemics.

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Section: A Case Studymentioning

confidence: 99%

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

et al. 2021

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“…Many researchers are also working toward intelligent management of communication systems. The traditional ML-based management methods are often inefficient due to their centralized data processing architecture and the risk of privacy leakage [5] . FRL can play an important role in services slicing and access controlling to replace centralized ML methods.…”

Section: Frl For Communication Networkmentioning

confidence: 99%

“…Though a few survey papers on FL [4][5][6] 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

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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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“…The authors also discuss the use of fog computing to improve IIoT security. In [108], the authors present the significance of Federated Learning (FL) in IIoT. The authors discuss how distributed learning techniques can improve the security and computation cost of learning in IIoT.…”

Section: Contributions Of the Paper In Comparison With Other Survey Papersmentioning

confidence: 99%

Reliable Internet of Things: Challenges and Future Trends

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The Internet of Things (IoT) is a vital component of many future industries. By intelligent integration of sensors, wireless communications, computing techniques, and data analytics, IoT can increase productivity and efficiency of industries. Reliability of data transmission is key to realize several applications offered by IoT. In this paper, we present an overview of future IoT applications, and their major communication requirements. We provide a brief survey of recent work in four major areas of reliable IoT including resource allocation, latency management, security, and reliability metrics. Finally, we highlight some of the important challenges for reliable IoT related to machine learning techniques, 6G communications and blockchain based security that need further investigation and discuss related future directions.

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Federated Learning for Internet of Things: A Comprehensive Survey

Cited by 603 publications

References 160 publications

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Federated reinforcement learning: techniques, applications, and open challenges

Reliable Internet of Things: Challenges and Future Trends

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