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Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
[article]
2020
arXiv
pre-print
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In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. ...
We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications. ...
CONCLUSION In this paper, we propose PerFit, a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications with data privacy protection. ...
arXiv:2002.10671v3
fatcat:4hjdd5ledzhdppdkk3cpfhd7nm
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
2020
IEEE Computer Graphics and Applications
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In this paper we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. ...
We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications. ...
CONCLUSION In this paper, we propose PerFit, a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications with data privacy protection. ...
doi:10.1109/ojcs.2020.2993259
pmid:32396074
fatcat:zttxcblzzrbi3piwp4l7cccvei
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2111.07494v2 fatcat:fzl6rz7mb5en3ivifjdue4t3dyOther Versions
The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. ...
Federated Learning (FL) has emerged as the most promising alternative approach to this problem. ...
Fig. 2 : 2 Fig. 2: Advantages of Federated Learning for IoT.
Fig. 3 : 3 Fig. 3: Applications of Federated Learning for IoT.
Fig. 4 : 4 Fig. 4: Challenges of Federated Learning for IoT. ...
arXiv:2111.07494v4
fatcat:ytrs6b4ob5cdnmqbyfq3gg4esu
Guest Editorial: Special Issue on Blockchain and Edge Computing Techniques for Emerging IoT Applications
2021
IEEE Internet of Things Journal
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Focusing on the research of social-aware cloud computing, cooperative cell caching, and mobile traffic offloading, he has authored over 100 technical papers in ...
edge clouds by proposing a chain-based service request model for emerging IoT applications. ...
and privacy issues in IoT, the article "Blockchain-based edge computing resource allocation in IoT: A deep reinforcement learning approach" by He et al. proposes a general framework for blockchain-based ...
doi:10.1109/jiot.2021.3050050
fatcat:rux57gjppjdqla556myxnvp4ve
Edge Intelligence: Architectures, Challenges, and Applications
[article]
2020
arXiv
pre-print
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Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. ...
This survey article provides a comprehensive introduction to edge intelligence and its application areas. ...
If mobile users upload their personal data to the cloud for a specific intelligent application, they would take the risk of privacy leakage, i.e., the personal data might be extracted by malicious hackers ...
arXiv:2003.12172v2
fatcat:xbrylsvb7bey5idirunacux6pe
Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture
2019
Sensors
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Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. ...
Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications. ...
In the majority of cases, cloud-to-edge dynamic and transparent data processing is not yet a reality, and this is a major drawback for the further adoption and uptake of IoT applications. ...
doi:10.3390/s19163591
fatcat:qz6md7lbsfhejcrs3e7vqigsnu
Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2101.10569v1 fatcat:kyt6ivau3fcgfmyxsrguljrpluOther Versions
In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. ...
The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. ...
federated learning for IIoT based on AI [101] . ...
arXiv:2101.10569v2
fatcat:xwebjbcvcjhbbaehajhdfjocia
Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis
2021
IEEE Access
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For enhancing cloud computing-based 5G heterogeneous network, Wei et al. [77] designed a federated learning scheme based on end-edge-cloud cooperation. ...
Tab. 3 presents the federated learning-based solution for cybersecurity in IoT applications.
II. FEDERATED MACHINE LEARNING APPROACHES FOR THE IOT APPLICATIONS
A. ...
Since July 2019, he has been a Visiting Senior Researcher with the NAU-Lincoln Joint Research Center of Intelligent Engineering, Nanjing Agricultural University, Nanjing, China. ...
doi:10.1109/access.2021.3118642
fatcat:222fgsvt3nh6zcgm5qt4kxe7c4
Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
[article]
2021
arXiv
pre-print
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To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. ...
The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. ...
Acknowledgement We acknowledge the authors (Dinh, Fang, Pubudu) for the contribution of our (blockchain -big data) development. ...
arXiv:2110.04160v2
fatcat:3y2kmamdbrfmrjdxv3zh47yphu
The Applications of Blockchain in Artificial Intelligence
2021
Security and Communication Networks
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In recent times, there have also been attempts to utilize blockchain (a peer-to-peer distributed system) to facilitate AI applications, for example, in secure data sharing (for model training), preserving ...
There has been increased interest in applying artificial intelligence (AI) in various settings to inform decision-making and facilitate predictive analytics. ...
deep learning framework based on blockchain, which encourages participants to engage in training Federated learning Consensus [65] IoT Blockchain Propose a knowledge market of IoT based on edge-AI, which ...
doi:10.1155/2021/6126247
fatcat:7neic6vhnrfmhghhdjf634ozh4
Guest Editorial: AI and Machine Learning Solution Cyber Intelligence Technologies: New Methodologies and Applications
2020
IEEE Transactions on Industrial Informatics
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IEEE CLOUD COMPUT- ING, and Information Sciences (Elsevier), and served as a general chair and program chair for many international conferences. ...
security, cyber-enabled applications in healthcare, and computing for well-being. ...
A novel data transmission framework is presented for the security of data transmitted from IoT devices to edge cloud to central cloud. ...
doi:10.1109/tii.2020.2988944
fatcat:h7wqymdnybdnxcmllm75pxynge
A Distributed Framework to Orchestrate Video Analytics Applications
[article]
2020
arXiv
pre-print
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To address the above-mentioned challenges, we propose a distributed framework to orchestrate video analytics across Edge and Cloud resources. ...
This paradigm shift has caught everyones attention in a large class of applications, including IoT-based video analytics using smart doorbells. ...
ACKNOWLEDGMENTS This publication has emanated from research supported in part by a research grant from Science Foundation Ireland under Grant Numbers 16/RC/3918 (Confirm SFI Research Centre for Smart Manufacturing ...
arXiv:2009.09065v1
fatcat:26dxl5l57zhirbg6o3iwst2fim
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues
2020
IEEE Computer Graphics and Applications
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Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the ...
FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. ...
In [34] , a FL-based imitation learning framework is proposed for cloud robotic systems with heterogeneous sensor data. ...
doi:10.1109/ojcs.2020.2992630
pmid:32386144
fatcat:uic45awlkneihkybfu7wnc26me
Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
2022
Future Internet
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; (3) Resources, Sharing, Storage and Energy Considerations for Crowd-IoT; and (4) Applications for Crowd-IoT. ...
This paper presents a comprehensive survey for this new Crowdsourcing IoT paradigm from four different perspectives: (1) Architectures for Crowd-IoT; (2) Trustworthy, Privacy and Security for Crowd-IoT ...
The authors designed and implemented Medusa, which is a novel programming framework to provide high-level abstractions for specifying the steps for a crowd-sensing task. Medusa ...
doi:10.3390/fi14020049
fatcat:o62tjaayyvfnjc75irzkpzu5vy
Internet of Things 2.0: Concepts, Applications, and Future Directions
2021
IEEE Access
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b: Federated Learning Federated learning is a technique of multiple users training a common machine learning model without leaking their local dataset to other users [99] . ...
Reinforcement learning algorithms aim to provide high-level intelligence to IoT applications. ...
doi:10.1109/access.2021.3078549
fatcat:g5jkc5p6tngpfonbhtsbcjipai
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