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Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework [article]

Qiong Wu and Kaiwen He and Xu Chen
2020 arXiv   pre-print
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

Qiong Wu, Kaiwen He, Xu Chen
2020 IEEE Computer Graphics and Applications  
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]

Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, Salman Avestimehr
2022 arXiv   pre-print
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

Victor C. M. Leung, Xiaofei Wang, F. Richard Yu, Dusit Niyato, Tarik Taleb, Sangheon Pack
2021 IEEE Internet of Things Journal  
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]

Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, Pan Hui
2020 arXiv   pre-print
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

Panagiotis Trakadas, Nikolaos Nomikos, Emmanouel T. Michailidis, Theodore Zahariadis, Federico M. Facca, David Breitgand, Stamatia Rizou, Xavi Masip, Panagiotis Gkonis
2019 Sensors  
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]

Bin Jiang, Jianqiang Li, Guanghui Yue, Houbing Song
2021 arXiv   pre-print
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

Mohamed Amine Ferrag, Othmane Friha, Leandros Maglaras, Helge Janicke, Lei Shu
2021 IEEE Access  
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]

Thippa Reddy Gadekallu, Quoc-Viet Pham, Thien Huynh-The, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Madhusanka Liyanage
2021 arXiv   pre-print
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

Ruonan Wang, Min Luo, Yihong Wen, Lianhai Wang, Kim-Kwang Raymond Choo, Debiao He, Wenxiu Ding
2021 Security and Communication Networks  
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

Ke Yan, Lu Liu, Yong Xiang, Qun Jin
2020 IEEE Transactions on Industrial Informatics  
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]

Tapan Pathak and Vatsal Patel and Sarth Kanani and Shailesh Arya and Pankesh Patel and Muhammad Intizar Ali and John Breslin
2020 arXiv   pre-print
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

Zhaoyang Du, Celimuge Wu, Tsutomu Yoshinaga, Kok-Lim Alvin Yau, Yusheng Ji, Jie Li
2020 IEEE Computer Graphics and Applications  
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

Kenneth Li Minn Ang, Jasmine Kah Phooi Seng, Ericmoore Ngharamike
2022 Future Internet  
; (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

Ian Zhou, Imran Makhdoom, Negin Shariati, Muhammad Ahmad Raza, Rasool Keshavarz, Justin Lipman, Mehran Abolhasan, Abbas Jamalipour
2021 IEEE Access  
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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