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Federated Learning in Smart City Sensing: Challenges and Opportunities

Ji Chu Jiang, Burak Kantarci, Sema Oktug, Tolga Soyata
2020 Sensors  
To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection.  ...  A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights  ...  This is most evident when incorporating Federated Learning methodologies for Mobile Crowdsensing tasks, by bridging the gap between data sensing machine learning model training while preserving user privacy  ... 
doi:10.3390/s20216230 pmid:33142863 pmcid:PMC7662977 fatcat:gl7qvweau5gzjmld6n6jabxcee

Reliable Federated Learning for Mobile Networks [article]

Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, Mohsen Guizani
2019 arXiv   pre-print
providing privacy preservation for mobile users.  ...  In the federated learning, training data is widely distributed and maintained on the mobile devices as workers.  ...  Mobile applications with federated learning perform model training by using these data without the need of data aggregation for privacy preservation.  ... 
arXiv:1910.06837v1 fatcat:txiaqizl4zgsngiqusz2h6sv2a

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing [article]

Qin Hu, Zhilin Wang, Minghui Xu, Xiuzhen Cheng
2021 arXiv   pre-print
Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated  ...  Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost.  ...  Index Terms-Mobile crowdsensing, federated learning, data privacy, blockchain, game theory. I.  ... 
arXiv:2110.08671v1 fatcat:fpnp3vfhhbb4fowpjgdciamzou

Federated Learning for Internet of Things: A Comprehensive Survey [article]

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 arXiv   pre-print
Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing  ...  Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need  ...  The study in [94] shows an FL-based mobile crowdsensing scheme, with a focus on privacy-preserving XGBoost training with the cooperation of multiple mobile users.  ... 
arXiv:2104.07914v1 fatcat:b5wsrfcbynel7jqdxpfw4ftwh4

Federated Learning for Internet of Things: A Comprehensive Survey

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 IEEE Communications Surveys and Tutorials  
Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing  ...  Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need  ...  The study in [94] shows an FL-based mobile crowdsensing scheme, with a focus on privacy-preserving XGBoost training with the cooperation of multiple mobile users.  ... 
doi:10.1109/comst.2021.3075439 fatcat:ycq2zydqrzhibfqyo4vzloeoqy

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  
. 3) When Federated Learning Meets Blockchain: The study shows that blockchain technology can be integrated with federated deep learning for cyber security in IoT networks.  ...  To provide privacy and data security, Taïk developed a privacy-preserving data analytic system, where the federated learning at the centralized fog devices.  ... 
doi:10.1109/access.2021.3118642 fatcat:222fgsvt3nh6zcgm5qt4kxe7c4

A Survey on Social-Physical Sensing [article]

Md Tahmid Rashid, Na Wei, Dong Wang
2021 arXiv   pre-print
., through social media or crowdsensing apps) to perceive the environment.  ...  This paper intends to bridge the knowledge gap in current literature by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.  ...  With the intent for preserving privacy and reducing network bandwidth requirements, federated learning (FL) is gaining traction as a decentralized AI training paradigm [271] , [272] , where a shared  ... 
arXiv:2104.01360v1 fatcat:b6sag5objzhezcfhscb3ctf2ca

Security and Privacy in IoT Using Machine Learning and Blockchain: Threats Countermeasures [article]

Nazar Waheed, Xiangjian He, Muhammad Ikram, Muhammad Usman, Saad Sajid Hashmi, Muhammad Usman
2020 arXiv   pre-print
systems.  ...  Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based  ...  The users do not know how and when their data may be used. To preserve these privacy issues, Shen et al. [115] proposed a fusion of machine learning with blockchain.  ... 
arXiv:2002.03488v4 fatcat:cxavellepncexgkfcs5phdj53u

Fairness in Federated Learning for Spatial-Temporal Applications [article]

Afra Mashhadi, Alex Kyllo, Reza M. Parizi
2022 arXiv   pre-print
Federated learning involves training statistical models over remote devices such as mobile phones while keeping data localized.  ...  Training in heterogeneous and potentially massive networks introduces opportunities for privacy-preserving data analysis and diversifying these models to become more inclusive of the population.  ...  Approaches such as Deep Federated Clustering (Mashhadi, Sterner, and Murray 2021) for unsupervised learning and IFCA (Ghosh et al. 2020) for supervised learning (when demographic labels are present at  ... 
arXiv:2201.06598v2 fatcat:t3q2wwv77jetfell5sacv4oawu

2020 Index IEEE Internet of Things Journal Vol. 7

2020 IEEE Internet of Things Journal  
., +, JIoT April 2020 2553-2562 Privacy-Preserving Federated Learning in Fog Computing.  ...  ., +, JIoT June 2020 5359-5370 Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach.  ...  ., +, JIoT Nov. 2020 11223-11237 Land mobile radio Physical-Layer Security in Space Information Networks: A Survey.  ... 
doi:10.1109/jiot.2020.3046055 fatcat:wpyblbhkrbcyxpnajhiz5pj74a

Table of contents

2018 IEEE Internet of Things Journal  
Tsang 4945 Human in the Loop: Distributed Deep Model for Mobile Crowdsensing . . . . . . . . . . . . . . L. Li, K. Ota, and M.  ...  Picano 5089 When Renewable Energy Meets LoRa: A Feasibility Analysis on Cable-Less Deployments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jiot.2018.2887290 fatcat:he4ogei7sngn5mp6zy5atezike

Federated Learning in Mobile Edge Networks: A Comprehensive Survey [article]

Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao
2020 arXiv   pre-print
Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced.  ...  Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications.  ...  privacy, there has been a growing effort on developing new privacy preserving distributed learning algorithms.  ... 
arXiv:1909.11875v2 fatcat:a2yxlq672needkejenu4j3izyu

Privacy-Preserving Federated Learning Framework with General Aggregation and Multiparty Entity Matching

Zhou Zhou, Youliang Tian, Changgen Peng, Zhipeng Cai
2021 Wireless Communications and Mobile Computing  
In this paper, we systematically propose a privacy-preserving federated learning framework (PFLF) where we first construct a general secure aggregation model in federated learning scenarios by combining  ...  The requirement for data sharing and privacy has brought increasing attention to federated learning.  ...  [24] propose a secret sharing-based federated extreme boosting learning framework to achieve privacypreserving model training for mobile crowdsensing. Xu et al.  ... 
doi:10.1155/2021/6692061 fatcat:lepqf3spw5ekzjl3coqpq4meo4

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems.  ...  Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics.  ...  They argue that there exist malicious mobile users who intentionally provide false sensing data to servers, to save costs and preserve their privacy, which in turn can make mobile crowdsensings systems  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems.  ...  Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience  ...  [464] Mobile crowdsensing Deep Q learning Mitigates vulnerabilities of mobile crowdsensing systems. Luong et al.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda
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