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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  
[102] developed a privacy-preserving federated learning scheme, which is based on blockchain technology for securing the Internet of Medical Things.  ...  2016 et al. [48] designed a federated learning scheme with strong privacy preservation, named HFWP, for VOLUME 4, 2016 Zone A Zone D Zone B Zone C Departure Arrival Aggregation Server  ...  ., and Habilitation degrees in computer science from Badji Mokhtar-Annaba University, Annaba, Algeria, in June, 2008, June, 2010, June, 2014, and April, 2019, respectively.  ... 
doi:10.1109/access.2021.3118642 fatcat:222fgsvt3nh6zcgm5qt4kxe7c4

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
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  ...  , and IoT privacy and security.  ...  FL for IoT Privacy and Security [101] IoT privacy preservation VFL - Vehicles Data centre An FL-based scheme for privacy preservation in vehicular IoT.  ... 
arXiv:2104.07914v1 fatcat:b5wsrfcbynel7jqdxpfw4ftwh4

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges [article]

Latif U. Khan, Walid Saad, Zhu Han, Ekram Hossain, Choong Seon Hong
2021 arXiv   pre-print
Third, we propose two IoT use cases of dispersed federated learning that can offer better privacy preservation than federated learning.  ...  In federated learning, only learning model updates are transferred between end-devices and the aggregation server.  ...  In dispersed federated learning, homomorphic encryption will give privacy preservation against a malicious sub-global aggregation server, whereas sub-global aggregations will give privacy preservation  ... 
arXiv:2009.13012v2 fatcat:4oqifqi5czfyxiqe7gjewmuzsq

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  
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  ...  , and IoT privacy and security.  ...  Fig. 8 . 8 Federated learning for blockchain-based crowdsensing in UAV networks. Fig. 9 . 9 FL-IoT application domains.  ... 
doi:10.1109/comst.2021.3075439 fatcat:ycq2zydqrzhibfqyo4vzloeoqy

HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [article]

Mohanad Sarhan, Wai Weng Lo, Siamak Layeghy, Marius Portmann
2022 arXiv   pre-print
In this paper, we propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection.  ...  The utilisation of Machine Learning (ML) capabilities in the defence against IoT cyber attacks has many potential benefits.  ...  However, data privacy is only preserved using federated learning.  ... 
arXiv:2204.04254v1 fatcat:k4hkj7cxrndizc6v5ca4dvcu2m

Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing [article]

Anudit Nagar
2019 arXiv   pre-print
Using these privacy aware ML Models at the core of a Federated Learning Ecosystem can enable the entire network to learn from data in a decentralized manner.  ...  In due course with the ever-evolving nature of newer statistical techniques infringing user privacy, machine learning models with algorithms built with respect for user privacy can offer a dynamically  ...  SUMMARY In this work we discuss the various segments of a blockchain based privacy-preserving federated learning network.  ... 
arXiv:1912.04859v1 fatcat:pqd3wqvmdjbvxkftdg26hv4sf4

Dispersed Federated Learning: Vision, Taxonomy, and Future Directions [article]

Latif U. Khan, Walid Saad, Zhu Han, Choong Seon Hong
2021 arXiv   pre-print
Other than privacy and robustness issues, federated learning over IoT networks requires a significant amount of communication resources for training.  ...  Federated learning offers on-device, privacy-preserving machine learning without the need to transfer end-devices data to a third party location.  ...  Other works in [4] and [5] proposed privacy preserving schemes based on over-the-air-computation.  ... 
arXiv:2008.05189v2 fatcat:7rg4fz3dhnb25jbjaln6wrvwcq

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  
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 this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT.  ...  A combination of blockchain and FL is introduced in [37] for a privacy-preserving data exchange in industrial IoT.  ... 
doi:10.1109/ojcs.2020.2992630 pmid:32386144 fatcat:uic45awlkneihkybfu7wnc26me

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  ...  Hence, in this paper, we perform a comprehensive review of how blockchain can benefit AI from these four aspects.  ...  architecture for privacy preserving schemes and the impact of privacy preserving schemes on performance. is reinforces the importance of designing lightweight privacy-preserving schemes.  ... 
doi:10.1155/2021/6126247 fatcat:7neic6vhnrfmhghhdjf634ozh4

Table of Contents

2022 IEEE Internet of Things Journal  
Srivastava 2545 Beyond Class-Level Privacy Leakage: Breaking Record-Level Privacy in Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Kwak 2578 Securing Critical IoT Infrastructures With Blockchain-Supported Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jiot.2022.3145222 fatcat:nj3gbnzw6vdqfbt43goja5xcoq

Table of contents

2021 IEEE Internet of Things Journal  
Meng 10766 Energy-Efficient Frame Aggregation Scheme in IoT Over Fiber-Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Zhang 10911 Hierarchical Federated Learning for Hybrid Data Partitioning Across Multitype Sensors . . . L. Su and V. K. N.  ... 
doi:10.1109/jiot.2021.3077780 fatcat:oi46xvj6efgtvefe3jrmkc4dwq

2020 Index IEEE Internet of Things Journal Vol. 7

2020 IEEE Internet of Things Journal  
., Rateless-Code-Based Secure Cooperative Transmission Scheme for Industrial IoT; JIoT July 2020 6550-6565 Jamalipour, A., see Murali, S., JIoT Jan. 2020 379-388 James, L.A., see Wanasinghe, T.R.,  ...  ., +, JIoT April 2020 2553-2562 Privacy-Preserving Federated Learning in Fog Computing.  ...  ., +, JIoT May 2020 3935-3947 LVPDA: A Lightweight and Verifiable Privacy-Preserving Data Aggregation Scheme for Edge-Enabled IoT.  ... 
doi:10.1109/jiot.2020.3046055 fatcat:wpyblbhkrbcyxpnajhiz5pj74a

Guest Editorial: Secure Communications Over the Internet of Artificially Intelligent Things

Zhihan Lv, Jaime Lloret, Houbing Song, Jun Shen, Wojciech Mazurczyk
2022 IEEE Internet of Things Magazine  
In the field of Artificial Intelligence of Things (AIoT), AI is realized through various technical supports, especially machine learning (ML) technology.  ...  ML can solve practical problems that are difficult for simple rules using data and is widely used in complex tasks, such as search engine (SE), autonomous driving (AD), machine translation (MT), medical  ...  In "FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things," the authors propose privacy-preserving decentralized FL for secure and  ... 
doi:10.1109/miot.2022.9773087 fatcat:vjplzcpjqbbb5lnwun76ircsqi

An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems [article]

Yu Xianjia, Jorge Peña Queralta, Jukka Heikkonen, Tomi Westerlund
2021 arXiv   pre-print
Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates.  ...  This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.  ...  In another work, an autonomous blockchain enabled FL has been proposed to add further privacy-preserving properties and efficient local on-vehicle machine learning model aggregation in a decentralized  ... 
arXiv:2104.10141v2 fatcat:x4mysoxyzzagpjxpzmpxu2af4q

Big Data Privacy in Smart Farming: A Review

Mohammad Amiri-Zarandi, Rozita A. Dara, Emily Duncan, Evan D. G. Fraser
2022 Sustainability  
In this paper, we present a holistic review of big data privacy in smart farming.  ...  The paper utilizes a data lifecycle schema and describes privacy concerns and requirements in smart farming in each of the phases of this data lifecycle.  ...  This scheme increases usability while preserving privacy. In [51] , an attribute-based encryption scheme was presented for big IoT data stored in the cloud.  ... 
doi:10.3390/su14159120 fatcat:fepfby2wkbfelfgspq4dami4aq
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