4.5 Article

Multi-tasking Federated Learning meets Blockchain to Foster Trust and Security in the Metaverse

Related references

Note: Only part of the references are listed.
Article Engineering, Multidisciplinary

MiTFed: A Privacy Preserving Collaborative Network Attack Mitigation Framework Based on Federated Learning Using SDN and Blockchain

Zakaria Abou El Houda et al.

Summary: This paper presents a novel framework called MiTFed, which utilizes federated learning, blockchain, and software-defined network technologies to collaboratively build a global intrusion detection model while preserving the privacy of collaborators. Experimental results show that MiTFed achieves high accuracy in detecting new emerging security threats, making it a promising framework for SDN.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2023)

Article Computer Science, Information Systems

Blockchain and trust-based clustering scheme for the IoV

Samiha Ayed et al.

Summary: The Internet of Vehicles (IoV) has led to a wide range of services for users, aiming to improve driving safety and user experience. However, the deployment of multiple technologies in the IoV ecosystem poses significant security challenges, including communication link security, device security, identity and liability, access control, and privacy concerns. Trust management is a key mechanism to enhance IoV security, and this paper proposes a blockchain-based trust management framework. By introducing a decentralized trust process and a clustering mechanism based on various parameters, such as trust value, safety distance, and energy factor, the proposed framework addresses scalability issues and considers the quality of service (QoS). Evaluation results demonstrate that the use of blockchain for forming trustworthy clusters enhances IoV reliability.

AD HOC NETWORKS (2023)

Review Computer Science, Theory & Methods

Blockchain for the metaverse: A Review

Thien Huynh-The et al.

Summary: Since Facebook officially changed its name to Meta in Oct. 2021, the metaverse has become a new norm of social networks and 3D virtual worlds. Blockchain is a promising solution for securing users' digital content and data in the metaverse due to its distinct features of decentralization, immutability, and transparency.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2023)

Article Computer Science, Information Systems

A node trust evaluation method of vehicle-road-cloud collaborative system based on federated learning

Denghui Wang et al.

Summary: In this paper, a trust evaluation scheme for the vehicle-road-cloud collaborative system based on Federated Learning (FLT) is proposed. The scheme addresses the challenges posed by heterogeneous networks and vulnerability to attacks, by combining a hierarchical trust evaluation model with federated learning. This enables personalized and reliable data transmission.

AD HOC NETWORKS (2023)

Proceedings Paper Computer Science, Theory & Methods

Federated Learning Meets Blockchain to Secure the Metaverse

Hajar Moudoud et al.

Summary: The development of the Metaverse is revolutionizing the way business is conducted in the physical world. By incorporating intelligent manufacturing and the Internet of Things (IoT), the Metaverse enables efficient data analysis and decision-making. However, the integration of the Metaverse with IoT presents challenges in sharing sensitive data. Federated learning (FL) using blockchain technology offers a solution to address this issue.

2023 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC (2023)

Article Computer Science, Information Systems

A Survey of Blockchain and Intelligent Networking for the Metaverse

Yuchuan Fu et al.

IEEE Internet of Things Journal (2022)

Article Computer Science, Information Systems

ESync: Accelerating Intra-Domain Federated Learning in Heterogeneous Data Centers

Zonghang Li et al.

Summary: This article proposes a new synchronization algorithm called ESync for isolated parties to collaborate in a shared data center, addressing the training inefficiency caused by strong computational heterogeneity. Extensive experiments demonstrate the effectiveness of ESync in terms of both training efficiency and convergence accuracy.

IEEE TRANSACTIONS ON SERVICES COMPUTING (2022)

Article Automation & Control Systems

When Federated Learning Meets Game Theory: A Cooperative Framework to Secure IIoT Applications on Edge Computing

Zakaria Abou El Houda et al.

Summary: Industry 5.0 is the next industrial revolution that relies on advanced digital technologies to improve production efficiency. Industrial Internet of Things (IIoT) and machine learning are important for detecting IIoT attacks, but there are challenges in building learning models and having up-to-date attack datasets. Multiaccess edge computing (MEC) and federated learning (FL) are promising technologies for protecting IIoT applications and dealing with attacks.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Article Engineering, Multidisciplinary

Detection and Prediction of FDI Attacks in IoT Systems via Hidden Markov Model

Hajar Moudoud et al.

Summary: This paper proposes a process for detecting and predicting false data injection (FDI) attacks in IoT systems. The process utilizes artificial intelligence to observe the behavior of IoT devices and predict their future actions, establishes trust between devices, and defends against attacks through bandwidth optimization and incentive mechanisms.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2022)

Proceedings Paper Engineering, Electrical & Electronic

MetaChain: A Novel Blockchain-based Framework for Metaverse Applications

Cong T. Nguyen et al.

Summary: In this paper, we propose MetaChain, a novel blockchain-based framework to address the challenges in the development of Metaverse applications. MetaChain utilizes smart contracts to manage complex interactions between the Metaverse Service Provider and users. We also design a new sharding scheme to improve scalability and use game theory to develop an incentive mechanism for user contributions to the Metaverse.

2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING) (2022)

Article Automation & Control Systems

Blockchain-Enhanced Data Sharing With Traceable and Direct Revocation in IIoT

Keping Yu et al.

Summary: This article proposes a blockchain-enhanced security access control scheme for IIoT in smart factories, which supports secure storage, access control, and tracking and revocation of malicious users.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Article Computer Science, Hardware & Architecture

Federated Edge Learning: Design Issues and Challenges

Afaf Tak et al.

Summary: Federated Learning is a distributed machine learning technique that has gained attention due to its benefits related to data privacy and scalability, but faces challenges when implemented at the network edge due to system and data heterogeneity and resource limitations. Designing FEEL algorithms for efficient learning poses various challenges, necessitating a multidisciplinary approach to address them. This article advocates for a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL and proposes a general framework for data-aware scheduling for future research directions.

IEEE NETWORK (2021)

Article Computer Science, Hardware & Architecture

B-ReST: Blockchain-Enabled Resource Sharing and Transactions in Fog Computing

Yang Gao et al.

Summary: This article introduces a new architecture for resource sharing and transactions in fog computing networks, called B-ReST, enabled by blockchain technology. Key technologies and advantages of B-ReST are discussed, and simulation results demonstrate the benefits of B-ReST in resource sharing and transactions through solving the RPM problem.

IEEE WIRELESS COMMUNICATIONS (2021)

Review Computer Science, Theory & Methods

A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions

Xuefei Yin et al.

Summary: In the past four years, federated learning (FL) has rapidly developed, but new privacy concerns have emerged during the aggregation of distributed intermediate results. Privacy-preserving federated learning (PPFL) is considered as a solution to privacy-preserving machine learning, but the challenge of protecting data privacy while maintaining data utility remains.

ACM COMPUTING SURVEYS (2021)

Article Automation & Control Systems

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

Wen Sun et al.

Summary: This article proposes a federated learning framework for Industrial IoT based on digital twins, which alleviates the impact of estimation errors caused by digital twins through a trusted aggregation method. The aggregation frequency of federated learning is adaptively adjusted through deep reinforcement learning, and a clustering-based asynchronous federated learning framework is proposed to adapt to the heterogeneity of Industrial IoT.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Proceedings Paper Computer Science, Hardware & Architecture

Data-Quality Based Scheduling for Federated Edge Learning

Afaf Taik et al.

Summary: In this paper, a data-quality based scheduling (DQS) algorithm is proposed for FEEL, prioritizing reliable devices with rich and diverse datasets to improve performance.

PROCEEDINGS OF THE IEEE 46TH CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN 2021) (2021)