Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges

Zheng, Zhaohua; Zhou, Yonggang; Sun, Yilong; Wang, Zhang; Liu, Boyi; Li, Keqiu

doi:10.1080/09540091.2021.1936455

Cited by 115 publications

(56 citation statements)

References 73 publications

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“…Through systematic experimental evaluation, the security and efficiency of AuthROS are verified. This work is also potential in some other fields as federated learning [34], [35], [38], cloud-edge cooperate robotics [36], [37],smart city, etc.…”

Section: Discussionmentioning

confidence: 99%

AuthROS: Secure Data Sharing Among Robot Operating Systems Based on Ethereum

Zhang

Tao

et al. 2022

Preprint

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The Robot Operating System (ROS) streamlines human processes, increasing the efficiency of various production tasks. However, the security of data transfer operations in ROS is still in its immaturity. Securing data exchange between several robots is a significant problem. This paper proposes \textit{AuthROS}, an Ethereum blockchain-based secure data sharing method, for robot communication. It is a ROS node authorization system capable of ensuring the immutability and security of private data flow between ROS nodes of any size. To ensure data security, AuthROS employs the smart contract for permission granting and identification, SM2-based key exchange, and SM4-based plaintext encryption techniques. In addition, we deploy a data digest upload technique to optimize data query and upload performance. Finally, the experimental findings reveal that AuthROS has strong security, time performance, and node forging in cases where data should be recorded and robots need to remain immobile.

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Section: Discussionmentioning

confidence: 99%

AuthROS: Secure Data Sharing Among Robot Operating Systems Based on Ethereum

Zhang

Tao

et al. 2022

Preprint

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“…FL acts a distributed learning model to ensure optimal learning, efficient utilization of collected raw data and transmission to a centralized place. FL also plays a significant role in advancement of smart cities as detailed by authors in [101]. Policy makers in urban smart cities can utilize FL to transmit the sensitive information gathered from IoT devices for effective management of priority assets.…”

Section: Fig 1 Federated Learning and Its Training Processmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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“…FL collectively trains algorithms on the device itself without exchanging the data with external entities. Most of the existing survey studies such as [24,25,26,27,28,29,31,32] are confined to explaining either general architecture, models, security, and privacy algorithms for FL or explaining how FL can be used in conjugation with other emerging technologies. Since FL has been successfully applied in conjugation with other emerging technologies, there is hardly a few survey work or studies such as [33,34,35,36] that have looked into FL and providing an overview on works that have tackled especially in the medical domain.…”

Section: A Scope and Contributionsmentioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar¹,

Hagos²,

Jha³

et al. 2022

Preprint

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With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. Hence, in recent years, data-driven medical applications have attracted a great deal of attention from academia to industry. This has undoubtedly improved the quality of healthcare delivery. However, AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards such as low latency. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. Additionally, the recent literature revealed that FL models ensure a high level of robustness and generalization compared to the traditional data-driven medical applications. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.

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Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges

Cited by 115 publications

References 73 publications

AuthROS: Secure Data Sharing Among Robot Operating Systems Based on Ethereum

AuthROS: Secure Data Sharing Among Robot Operating Systems Based on Ethereum

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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