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Long Luo - IEEE Xplore Author Profile

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Author details

Long Luo

Publications

45

Citations

519

Publications by Year

20152025

Co-Authors:

Yang BaiQingqing CaiWeibo CaiMeng-Hsi ChenMiao Dai

Show All Co-Authors (65)

Long Luo

Affiliation

School of Information and Communication Engineering

University of Electronic Science and Technology of China

Chengdu, China

Publication Topics

Parameter Server,
Distributed Machine Learning,
Edge Server,
Federated Learning,
Global Model,
Updated Model,
Wide Area Network,
Completion Time,
Convergence Rate,
Heterogeneous Data,
Link Capacity,
Local Training

Biography

Long Luo received the BS degree in communication engineering from the Xi'an University of Technology, in Jul. 2012, and the MS degree in communication engineering from the UESTC, in Jul. 2015. She is currently working toward the PhD degree from the University of Electronic Science and Technology of China (UESTC). Her research interests include datacenters, cloud computing, software-defined networks, and data-driven networking.(Based on document published on 20 August 2019).

Publications

45

Citations

519

Publications by Year

20152025

Co-Authors:

Yang Bai
Qingqing Cai
Weibo Cai
Meng-Hsi Chen
Miao Dai

Show All Co-Authors (65)

Author's Published Works

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Showing 1-25 of 45 results

Conferences (22)

Journals (18)

Early Access Articles (3)

Magazines (2)

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Long Luo(45)
Hongfang Yu(42)
Gang Sun(23)
Shouxi Luo(11)
Zonghang Li(9)
University of Electronic Science and Technology of China, Chengdu, China(9)
Peng Cheng Laboratory, Shenzhen, China(7)
Agile and Intelligent Computing Key Laboratory of Sichuan Province, Chengdu, China(5)
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China(5)
University of Electronic Science and Technology of China, P.R. China(4)
IEEE Transactions on Services Computing(4)
IEEE Network(3)
IEEE Transactions on Cloud Computing(3)
GLOBECOM 2022 - 2022 IEEE Global Communications Conference(2)
ICC 2022 - IEEE International Conference on Communications(2)
IEEE(45)
Media(3)
Rio de Janeiro, Brazil(2)
Seoul, Korea, Republic of(2)
Atlanta, GA, USA(1)
Austin, TX, USA(1)
Dublin, Ireland(1)
Completion Time(12)
Wide Area Network(12)
Global Model(9)
Link Capacity(9)
Admission Control(8)

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Domain-Specific Transport Protocols for In-Network Processing at the Edge: A Case Study of Accelerating Model Synchronization

Shouxi Luo;Peidong Zhang;Xin Song;Pingzhi Fan;Huanlai Xing;Long Luo;Hongfang Yu

IEEE Transactions on Mobile Computing

Year: 2025 | Early Access Article |

Nowadays, cross-device federated learning (FL) is the key to achieving personalization services for mobile users and has been widely employed by companies like Google, Microsoft, and Alibaba in production. With the explosive growth in the number of participants, the central FL server, which acts as the manager and aggregator of cross-device model training, would get overloaded, becoming the system...Show More

Domain-Specific Transport Protocols for In-Network Processing at the Edge: A Case Study of Accelerating Model Synchronization

Shouxi Luo;Peidong Zhang;Xin Song;Pingzhi Fan;Huanlai Xing;Long Luo;Hongfang Yu

IEEE Transactions on Mobile Computing

Year: 2025 | Early Access Article |

Deadline-aware Online Job Scheduling for Distributed Training in Heterogeneous Clusters

Yuchen Zhang;Long Luo;Gang Sun;Hongfang Yu;Bo Li

IEEE Transactions on Cloud Computing

Year: 2025 | Early Access Article |

The explosive growth in training data and model sizes has spurred the adoption of distributed deep learning (DL) in heterogeneous computing clusters. Efficiently scheduling distributed training jobs in such heterogeneous environments while ensuring they meet user-specified deadlines remains a critical challenge. While most existing works focus on reducing job completion time in homogeneous cluster...Show More

Deadline-aware Online Job Scheduling for Distributed Training in Heterogeneous Clusters

Yuchen Zhang;Long Luo;Gang Sun;Hongfang Yu;Bo Li

IEEE Transactions on Cloud Computing

Year: 2025 | Early Access Article |

Efficient Parameter Synchronization for Peer-to-Peer Distributed Learning With Selective Multicast

Shouxi Luo;Pingzhi Fan;Ke Li;Huanlai Xing;Long Luo;Hongfang Yu

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

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Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided that workers will eventually participate in the synchronizations, $i)$i) the training still converges, even if only $p$p workers take part in each round of synchronization, and $ii)$ii) a larger $p$p generally leads to a faster rate of convergence. These findings shed light on elimina...Show More

Efficient Parameter Synchronization for Peer-to-Peer Distributed Learning With Selective Multicast

Shouxi Luo;Pingzhi Fan;Ke Li;Huanlai Xing;Long Luo;Hongfang Yu

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

KlonetAI: Automating (Com)2Nets Management with Human Language Intents

Qing Li;Yanxu Xiong;Zonghang Li;Chongxi Ma;Hongfang Yu;Gang Sun;Long Luo;Zhaofeng Zhang

Year: 2024 | Early Access Article |

The communication and computation integrated network architecture ((Com)2Net) simplifies the deployment of applications such as distributed machine learning. However, the management and operation of the (Com)2Net pose significant burdens and consume a substantial amount of time due to its large-scale and complexity nature. Intent-based networking technology and Large Language Models (LLMs) are ben...Show More

KlonetAI: Automating (Com)2Nets Management with Human Language Intents

Qing Li;Yanxu Xiong;Zonghang Li;Chongxi Ma;Hongfang Yu;Gang Sun;Long Luo;Zhaofeng Zhang

Year: 2024 | Early Access Article |

Energy-Efficient Hierarchical Collaborative Learning Over LEO Satellite Constellations

Long Luo;Chi Zhang;Hongfang Yu;Zonghang Li;Gang Sun;Shouxi Luo

IEEE Journal on Selected Areas in Communications

Year: 2024 | Volume: 42, Issue: 12 | Journal Article |

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The hierarchical collaborative learning within Low Earth Orbit (LEO) satellite constellations, termed LEO-HCL, is gaining increasing popularity by integrating intra-orbit Inter-Satellite Links and orbital edge computing to alleviate the latency issues caused by intermittent satellite connectivity in satellite-ground training architectures. However, LEO-HCL systems are confronted with a triad of ch...Show More

Energy-Efficient Hierarchical Collaborative Learning Over LEO Satellite Constellations

Long Luo;Chi Zhang;Hongfang Yu;Zonghang Li;Gang Sun;Shouxi Luo

IEEE Journal on Selected Areas in Communications

Year: 2024 | Volume: 42, Issue: 12 | Journal Article |

Analysis and Optimization for Passive One-way Delay Measurement Tax in Container Networks

Jingzhao Xie;Chongxi Ma;Hongfang Yu;Long Luo;Gang Sun

2024 IEEE 17th International Conference on Cloud Computing (CLOUD)

Year: 2024 | Conference Paper |

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Container networks have become crucial to the overall performance and health of network systems due to the increasing adoption of container technologies. Passive per-packet and per-hop one-way delay (p4 h-OWD) measurement is essential for prompt and accurate detection and localization of network issues in container environments. Existing measurement methods for generic virtual networks require int...Show More

Analysis and Optimization for Passive One-way Delay Measurement Tax in Container Networks

Jingzhao Xie;Chongxi Ma;Hongfang Yu;Long Luo;Gang Sun

2024 IEEE 17th International Conference on Cloud Computing (CLOUD)

Year: 2024 | Conference Paper |

Accelerating Geo-Distributed Machine Learning With Network-Aware Adaptive Tree and Auxiliary Route

Zonghang Li;Wenjiao Feng;Weibo Cai;Hongfang Yu;Long Luo;Gang Sun;Hongyang Du;Dusit Niyato

IEEE/ACM Transactions on Networking

Year: 2024 | Volume: 32, Issue: 5 | Journal Article |

Cited by: Papers (1)

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Distributed machine learning is becoming increasingly popular for geo-distributed data analytics, facilitating the collaborative analysis of data scattered across data centers in different regions. This paradigm eliminates the need for centralizing sensitive raw data in one location but faces the significant challenge of high parameter synchronization delays, which stems from the constraints of ba...Show More

Accelerating Geo-Distributed Machine Learning With Network-Aware Adaptive Tree and Auxiliary Route

Zonghang Li;Wenjiao Feng;Weibo Cai;Hongfang Yu;Long Luo;Gang Sun;Hongyang Du;Dusit Niyato

IEEE/ACM Transactions on Networking

Year: 2024 | Volume: 32, Issue: 5 | Journal Article |

Communication-Efficient Federated Learning With Adaptive Aggregation for Heterogeneous Client-Edge-Cloud Network

Long Luo;Chi Zhang;Hongfang Yu;Gang Sun;Shouxi Luo;Schahram Dustdar

IEEE Transactions on Services Computing

Year: 2024 | Volume: 17, Issue: 6 | Journal Article |

Cited by: Papers (4)

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Client-edge-cloud Federated Learning (CEC-FL) is emerging as an increasingly popular FL paradigm, alleviating the performance limitations of conventional cloud-centric Federated Learning (FL) by incorporating edge computing. However, improving training efficiency while retaining model convergence is not easy in CEC-FL. Although controlling aggregation frequency exhibits great promise in improving ...Show More

Communication-Efficient Federated Learning With Adaptive Aggregation for Heterogeneous Client-Edge-Cloud Network

Long Luo;Chi Zhang;Hongfang Yu;Gang Sun;Shouxi Luo;Schahram Dustdar

IEEE Transactions on Services Computing

Year: 2024 | Volume: 17, Issue: 6 | Journal Article |

Joint Demand Forecasting and Network Slice Pricing for Profit Maximization in Network Slicing

Qing Li;Yuhui Wang;Gang Sun;Long Luo;Hongfang Yu

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 2 | Journal Article |

Cited by: Papers (3)

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As a key technology of next-generation networks, network slicing enables networks to flexibly and efficiently fulfill the heterogeneous requirements of various services. In the slice-as-a-service business model, the service provider (SP) creates network slices to meet the service level agreements (SLAs) between the SP and users. In this article, we propose an SLA guaranteed network slicing framewo...Show More

Joint Demand Forecasting and Network Slice Pricing for Profit Maximization in Network Slicing

Qing Li;Yuhui Wang;Gang Sun;Long Luo;Hongfang Yu

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 2 | Journal Article |

FedGSync: Jointly Optimized Weak Synchronization and Gradient Transmission for Fast Distributed Machine Learning in Heterogeneous WAN

Huaman Zhou;Yihong He;Zhihao Zhang;Long Luo;Hongfang Yu;Gang Sun

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2023 | Conference Paper |

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Due to privacy and cost reasons, distributed machine learning in Wide-Area Networks(DML-WAN) is becoming an emerging and popular collaborative learning paradigm. However, heterogeneity in computing power and data distribution among workers in different locations has a dramatic impact on training performance, including convergence speed and learning accuracy. Most of the existing works on distribut...Show More

FedGSync: Jointly Optimized Weak Synchronization and Gradient Transmission for Fast Distributed Machine Learning in Heterogeneous WAN

Huaman Zhou;Yihong He;Zhihao Zhang;Long Luo;Hongfang Yu;Gang Sun

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2023 | Conference Paper |

Performance Analysis of Non-ideal Wireless PBFT Networks with mmWave and Terahertz Signals

Haoxiang Luo;Xiangyue Yang;Hongfang Yu;Gang Sun;Shizhong Xu;Long Luo

2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom)

Year: 2023 | Conference Paper |

Cited by: Papers (8)

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Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) is seen as the most applicable consensus mechanism in blockchain-enabled wireless networks. However, previous studies on PBFT do not consider the channel performance of the physical layer, such as path loss and channel fading, result...Show More

Performance Analysis of Non-ideal Wireless PBFT Networks with mmWave and Terahertz Signals

Haoxiang Luo;Xiangyue Yang;Hongfang Yu;Gang Sun;Shizhong Xu;Long Luo

2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom)

Year: 2023 | Conference Paper |

NBSync: Parallelism of Local Computing and Global Synchronization for Fast Distributed Machine Learning in WANs

Huaman Zhou;Zonghang Li;Hongfang Yu;Long Luo;Gang Sun

IEEE Transactions on Services Computing

Year: 2023 | Volume: 16, Issue: 6 | Journal Article |

Cited by: Papers (2)

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Recently, due to privacy concerns, distributed machine learning in Wide-Area Networks (DML-WANs) attracts increasing attention and has been widely deployed to promote the widespread application of intelligence services that rely on geographically distributed data. DML-WANs is essentially performing collaboratively federated learning over a combination of servers at both edge and cloud on a large s...Show More

NBSync: Parallelism of Local Computing and Global Synchronization for Fast Distributed Machine Learning in WANs

Huaman Zhou;Zonghang Li;Hongfang Yu;Long Luo;Gang Sun

IEEE Transactions on Services Computing

Year: 2023 | Volume: 16, Issue: 6 | Journal Article |

High Throughput Routing Path Selection for Payment Channel Network

Qingqing Cai;Gang Sun;Hongfang Yu;Long Luo

2023 IEEE Symposium on Computers and Communications (ISCC)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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The Payment Channel Network (PCN) has emerged as a prominent solution for addressing the scalability limitations of cryptocurrencies. A critical aspect of PCN transactions is the selection of appropriate routing paths between parties. However, the success rate of payment routing in PCNs is significantly influenced by channel capacity and other payments, making path selection in PCNs more challengi...Show More

High Throughput Routing Path Selection for Payment Channel Network

Qingqing Cai;Gang Sun;Hongfang Yu;Long Luo

2023 IEEE Symposium on Computers and Communications (ISCC)

Year: 2023 | Conference Paper |

HFedMS: Heterogeneous Federated Learning With Memorable Data Semantics in Industrial Metaverse

Shenglai Zeng;Zonghang Li;Hongfang Yu;Zhihao Zhang;Long Luo;Bo Li;Dusit Niyato

IEEE Transactions on Cloud Computing

Year: 2023 | Volume: 11, Issue: 3 | Journal Article |

Cited by: Papers (28)

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Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, ...Show More

HFedMS: Heterogeneous Federated Learning With Memorable Data Semantics in Industrial Metaverse

Shenglai Zeng;Zonghang Li;Hongfang Yu;Zhihao Zhang;Long Luo;Bo Li;Dusit Niyato

IEEE Transactions on Cloud Computing

Year: 2023 | Volume: 11, Issue: 3 | Journal Article |

Cost-Efficient Scheduling of Multicast Transfers With Deadline Guarantees Across Edge Datacenters

Long Luo;Qixuan Jin;Jingzhao Xie;Gang Sun;Hongfang Yu

IEEE Transactions on Services Computing

Year: 2023 | Volume: 16, Issue: 1 | Journal Article |

Cited by: Papers (2)

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Moderate-scale datacenters are increasingly deployed at the network edge to support low-latency and high-bandwidth Internet of Things (IoT) and 5G applications. These applications usually have bulk data to transfer from one datacenter to several datacenters, which can cause a significant amount of bandwidth costs. Traffic engineering (TE) systems at the network edge must minimize the bandwidth cos...Show More

Cost-Efficient Scheduling of Multicast Transfers With Deadline Guarantees Across Edge Datacenters

Long Luo;Qixuan Jin;Jingzhao Xie;Gang Sun;Hongfang Yu

IEEE Transactions on Services Computing

Year: 2023 | Volume: 16, Issue: 1 | Journal Article |

MMOS: Multi-Staged Mutation Operator Scheduling for Deep Learning Library Testing

Junqiang Li;Senyi Li;Jiawei Wu;Long Luo;Yang Bai;Hongfang Yu

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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The rapid development of deep learning (DL) technology has made deep learning libraries such as Tensor Flow widely used in practice. However, the complexity of DL libraries inevitably leads to multiple vulnerabilities. Recently, research on DL library testing generally designs a variety of mutation operators to generate new models from the perspective of model mutation. However, we find these rese...Show More

MMOS: Multi-Staged Mutation Operator Scheduling for Deep Learning Library Testing

Junqiang Li;Senyi Li;Jiawei Wu;Long Luo;Yang Bai;Hongfang Yu

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

vNetRadar: Lightweight and Network-Wide Traffic Measurement in Virtual Networks

Tie Ma;Jin Zhang;Long Luo;Hongfang Yu;Gang Sun;Jian Sun

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Measuring traffic metrics is indispensable in virtual networks as it is the basis for a wide range of applications, such as network diagnostics and performance evaluation of the network algorithms. However, existing measurement schemes fail to have all these excellent characteristics simultaneously: 1) fine-grained, i.e. to obtain per packet level information. 2) lightweight, namely low CPU and ba...Show More

vNetRadar: Lightweight and Network-Wide Traffic Measurement in Virtual Networks

Tie Ma;Jin Zhang;Long Luo;Hongfang Yu;Gang Sun;Jian Sun

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

DeepCov: Coverage Guided Deep Learning Framework Fuzzing

Jiawei Wu;Senyi Li;Junqiang Li;Long Luo;Hongfang Yu;Gang Sun

2022 7th IEEE International Conference on Data Science in Cyberspace (DSC)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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The large-scale use of deep learning (DL) technology makes it important to ensure software security in DL systems. All DL systems depend on the DL framework to achieve development tasks. If any vulnerability exists in the DL framework, DL systems will contain potential security risks. In addition, existing DL framework testing methods typically use inconsistencies as the feedback and cannot effect...Show More

DeepCov: Coverage Guided Deep Learning Framework Fuzzing

Jiawei Wu;Senyi Li;Junqiang Li;Long Luo;Hongfang Yu;Gang Sun

2022 7th IEEE International Conference on Data Science in Cyberspace (DSC)

Year: 2022 | Conference Paper |

Game Theoretic Approach for Multipriority Data Transmission in 5G Vehicular Networks

Gang Sun;Li Sheng;Long Luo;Hongfang Yu

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 12 | Journal Article |

Cited by: Papers (59)

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The vehicle-to-vehicle (V2V) communication driven by the fifth generation (5G) cellular mobile network with the features of ultra-high reliability and low latency provides promising solutions to various applications in the intelligent transportation system (ITS). To improve the resource utilization and guarantee the quality-of-service (QoS), users in 5G vehicular networks have to select appropriat...Show More

Game Theoretic Approach for Multipriority Data Transmission in 5G Vehicular Networks

Gang Sun;Li Sheng;Long Luo;Hongfang Yu

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 12 | Journal Article |

Fast Parameter Synchronization for Distributed Learning with Selective Multicast

Shouxi Luo;Pingzhi Fan;Ke Li;Huanlai Xing;Long Luo;Hongfang Yu

ICC 2022 - IEEE International Conference on Communications

Year: 2022 | Conference Paper |

Cited by: Papers (6)

HTML

Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided workers would participate in the synchronizations eventually, i) the training still converges, even if only p workers take part in each round of synchronization, and ii) a larger p generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck...Show More

Fast Parameter Synchronization for Distributed Learning with Selective Multicast

Shouxi Luo;Pingzhi Fan;Ke Li;Huanlai Xing;Long Luo;Hongfang Yu

ICC 2022 - IEEE International Conference on Communications

Year: 2022 | Conference Paper |

Eliminating Communication Bottlenecks in Cross-Device Federated Learning with In-Network Processing at the Edge

Shouxi Luo;Pingzhi Fan;Huanlai Xing;Long Luo;Hongfang Yu

ICC 2022 - IEEE International Conference on Communications

Year: 2022 | Conference Paper |

Cited by: Papers (7)

HTML

Nowadays, cross-device federated learning (FL) is the key to achieving personalization services for mobile users and has been widely employed by companies like Google, Microsoft, and Alibaba in production. With the explosive increase of participants, the central FL server, which acts as the manager and aggregator of cross-device model training, would get overloaded, becoming the system bottlenecks...Show More

Eliminating Communication Bottlenecks in Cross-Device Federated Learning with In-Network Processing at the Edge

Shouxi Luo;Pingzhi Fan;Huanlai Xing;Long Luo;Hongfang Yu

ICC 2022 - IEEE International Conference on Communications

Year: 2022 | Conference Paper |

PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks

Miao Dai;Long Luo;Jing Ren;Hongfang Yu;Gang Sun

IEEE Transactions on Network Science and Engineering

Year: 2022 | Volume: 9, Issue: 6 | Journal Article |

Cited by: Papers (66)

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5G/B5G is intended to support various services with the assistance of network slices, and each slice requires adequate resources to provide the prenegotiated service quality. The Slice Admission Control (SAC) algorithm is a necessity for Slice Providers (SPs) to guarantee the Quality of Service (QoS) and Quality of Experience (QoE) of each admitted request. In such circumstances, concerns regardin...Show More

PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks

Miao Dai;Long Luo;Jing Ren;Hongfang Yu;Gang Sun

IEEE Transactions on Network Science and Engineering

Year: 2022 | Volume: 9, Issue: 6 | Journal Article |

Flexible and Efficient Multicast Transfers in Inter-Datacenter Networks

Long Luo;Linjian Yu;Tie Ma;Hongfang Yu

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)

Year: 2022 | Conference Paper |

Cited by: Papers (4)

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The explosive growth of global distributed services has led to a massive increase in bulk multicast data transfers over the inter-datacenter Wide-Area Network. While many solutions have been proposed to improve the performance of inter-DC bulk data transfers, they are insufficient to optimize multicast transfers because they fail to explore the characteristics of multicast transfers and network to...Show More

Flexible and Efficient Multicast Transfers in Inter-Datacenter Networks

Long Luo;Linjian Yu;Tie Ma;Hongfang Yu

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)

Year: 2022 | Conference Paper |

Joint Client Selection and Resource Allocation for Federated Learning in Mobile Edge Networks

Long Luo;Qingqing Cai;Zonghang Li;Hongfang Yu

2022 IEEE Wireless Communications and Networking Conference (WCNC)

Year: 2022 | Conference Paper |

Cited by: Papers (6)

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Federated Learning (FL) has received widespread attention in 5G mobile edge networks (MENs) due to its ability to facilitate collaborative learning of machine learning models without revealing user privacy data. However, FL training is both time and energy consuming. Constrained by the instability and limited resources of clients in MENs, it is challenging to optimize both learning time and energy...Show More

Joint Client Selection and Resource Allocation for Federated Learning in Mobile Edge Networks

Long Luo;Qingqing Cai;Zonghang Li;Hongfang Yu

2022 IEEE Wireless Communications and Networking Conference (WCNC)

Year: 2022 | Conference Paper |

Blockchain-Enabled Two-Way Auction Mechanism for Electricity Trading in Internet of Electric Vehicles

Long Luo;Jingcui Feng;Hongfang Yu;Gang Sun

IEEE Internet of Things Journal

Year: 2022 | Volume: 9, Issue: 11 | Journal Article |

Cited by: Papers (47)

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As people pay more attention to environment protection, the number of electric vehicles (EVs) is gradually increasing. Energy trading management for EVs is becoming a challenge. However, existing research has not considered the problem of information sharing between energy traders and issues surrounding the protection of user privacy. Therefore, in this article, we propose a vehicle-to-vehicle (V2...Show More

Blockchain-Enabled Two-Way Auction Mechanism for Electricity Trading in Internet of Electric Vehicles

Long Luo;Jingcui Feng;Hongfang Yu;Gang Sun

IEEE Internet of Things Journal

Year: 2022 | Volume: 9, Issue: 11 | Journal Article |

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