4.7 Article

Resource management in multi-heterogeneous cluster networks using intelligent intra-clustered federated learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Telecommunications

An intelligent Hybrid-Q Learning clustering approach and resource management within heterogeneous cluster networks based on reinforcement learning

Fahad Razaque Mughal et al.

Summary: In this article, an intelligent Hybrid-Q Learning approach is proposed for IoT and WSN in heterogeneous cluster networks. By leveraging the self-learning abilities of HCNs, this approach creates efficient resource utilization and node communication performance, while reducing energy consumption through increased throughput and link management. Experimental results show high accuracy, low-level complexity, fast dynamic response times, and scalability for dynamic IoT environments and WSNs.

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES (2023)

Article Engineering, Electrical & Electronic

Federated Learning With Non-IID Data in Wireless Networks

Zhongyuan Zhao et al.

Summary: This paper studies federated learning with non-IID data in wireless networks and proposes a federated averaging scheme and a joint optimization algorithm to reduce distribution divergence, maintaining a balance between model accuracy and cost. Simulation results show significant performance gains with a small price of latency and energy consumption.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2022)

Article Computer Science, Hardware & Architecture

A new Asymmetric Link Quality Routing protocol (ALQR) for heterogeneous WSNs

Fahad Razaque Mughal et al.

Summary: This paper introduces an intelligent Asymmetry link Quality Routing (ALQR) protocol for challenging receiver-initiated asynchronous heterogeneous networks with low energy consumption clustering protocols. According to the findings, ALQR outperforms in terms of region and lifetime, while ensuring overall throughput.

MICROPROCESSORS AND MICROSYSTEMS (2022)

Article Automation & Control Systems

Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

Peiying Zhang et al.

Summary: This study explores the efficient and secure management of massive user data generated by IIoT devices, utilizing FL technology and DRL algorithms to increase model aggregation rates and reduce communication costs.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Article Computer Science, Theory & Methods

A survey on security and privacy of federated learning

Viraaji Mothukuri et al.

Summary: Federated learning (FL) is a new type of artificial intelligence that builds upon decentralized data and training, currently in its infancy, facing security and privacy concerns that need to be carefully evaluated and documented.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2021)

Article Computer Science, Information Systems

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Yulei Wu et al.

Summary: Critical infrastructure systems in Industry 4.0 are facing challenges in security and scalability. The convergence of blockchain and edge computing is expected to address these issues and provide secure and scalable critical infrastructures. Current research is mainly focused on security, privacy, and scalability in this area.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Computer Science, Artificial Intelligence

Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

Felix Sattler et al.

Summary: Federated learning is widely used for collaborative training of machine learning models under privacy constraints, but can yield suboptimal results when local data distributions diverge. Clustered FL is a novel framework that addresses this issue by grouping clients with jointly trainable data distributions based on geometric properties of the FL loss surface.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Computer Science, Information Systems

Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

Dinh C. Nguyen et al.

Summary: The article discusses the concept of FLchain in MEC networks, focusing on privacy protection, security, cross-device collaboration, and resource allocation. FLchain integrates FL and blockchain technology, presenting a promising paradigm for intelligent MEC networks.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Engineering, Electrical & Electronic

Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

Yunlong Lu et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Article Computer Science, Artificial Intelligence

Machine learning and data analytics for the IoT

Erwin Adi et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Computer Science, Information Systems

Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

Yi Liu et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Computer Science, Artificial Intelligence

FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

Yiqiang Chen et al.

IEEE INTELLIGENT SYSTEMS (2020)

Article Automation & Control Systems

A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems

Pathum Chamikara Mahawaga Arachchige et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Engineering, Electrical & Electronic

Wireless Communications for Collaborative Federated Learning

Mingzhe Chen et al.

IEEE COMMUNICATIONS MAGAZINE (2020)

Article Construction & Building Technology

Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city

Saurabh Singh et al.

SUSTAINABLE CITIES AND SOCIETY (2020)

Article Computer Science, Information Systems

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Jingjing Wang et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2020)

Article Computer Science, Information Systems

Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

Latif U. Khan et al.

IEEE ACCESS (2020)

Article Engineering, Electrical & Electronic

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

Zhi Zhou et al.

PROCEEDINGS OF THE IEEE (2019)