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Xiangjun Xin - IEEE Xplore Author Profile

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Author details

Xiangjun Xin

Publications

7

Citations

63

Publications by Year

20082025

Co-Authors:

Yuling ChenXiu-Bo ChenYafeng ChenMianxiong DongMin Huang

Show All Co-Authors (18)

Xiangjun Xin

Affiliation

College of Software Engineering

Zhengzhou University of Light Industry

Zhengzhou, China

Publication Topics

Public Key,
Security Proof,
Data Privacy,
Data Security,
Cardinality,
Cloud Computing,
Communication Cost,
Computational Burden,
Computational Cost,
Concrete Construction,
Contact Tracing,
Hash Function

Biography

Xiangjun Xin received the Ph.D. degree from Xidian University, Xi’an, China, in 2009.,He is currently a Professor with the College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, China. His current research interests include quantum cryptography and information security.(Based on document published on 27 February 2024).

Publications

7

Citations

63

Publications by Year

20082025

Co-Authors:

Yuling Chen
Xiu-Bo Chen
Yafeng Chen
Mianxiong Dong
Min Huang

Show All Co-Authors (18)

Author's Published Works

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Showing 1-4 of 4 results

Conferences (4)

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Single Year
Hui Jiang(4)
Min Liu(3)
Yuwei Wang(3)
Sheng Sun(3)
Xuefeng Jiang(2)
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China(3)
University of Chinese Academy of Sciences, Beijing, China(3)
Zhongguancun Laboratory, Beijing, China(2)
Lenovo Research, China(1)
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University, Xi’an, China(1)
2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)(2)
2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)(1)
2024 43rd Chinese Control Conference (CCC)(1)
IEEE(4)
St. Petersburg, FL, USA(2)
Kunming, China(1)
Oslo, Norway(1)
Federated Learning(3)
Local Training(3)
Communication Rounds(2)
Convolutional Neural Network(2)
Deep Neural Network(2)

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DNLight: Learning Efficient Evaluation for Traffic Signal Control

Hui Jiang;Meiqin Liu;Senlin Zhang;Ronghao Zheng;Xuguang Lan

2024 43rd Chinese Control Conference (CCC)

Year: 2024 | Conference Paper |

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Traffic signal control (TSC) is an effective way to alleviate traffic congestion. Since traditional methods cannot adapt to complex and changeable dynamic traffic flows, reinforcement learning (RL) methods have attracted widespread attention from researchers. However, due to the lack of efficient policy evaluation modules, the evolution process faces the challenges of long learning time and slow c...Show More

DNLight: Learning Efficient Evaluation for Traffic Signal Control

Hui Jiang;Meiqin Liu;Senlin Zhang;Ronghao Zheng;Xuguang Lan

2024 43rd Chinese Control Conference (CCC)

Year: 2024 | Conference Paper |

FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization

Xujing Li;Min Liu;Sheng Sun;Yuwei Wang;Hui Jiang;Xuefeng Jiang

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local models and the server-side global model. H...Show More

FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization

Xujing Li;Min Liu;Sheng Sun;Yuwei Wang;Hui Jiang;Xuefeng Jiang

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2023 | Conference Paper |

FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

Jingjing Xue;Min Liu;Sheng Sun;Yuwei Wang;Hui Jiang;Xuefeng Jiang

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dr...Show More

FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

Jingjing Xue;Min Liu;Sheng Sun;Yuwei Wang;Hui Jiang;Xuefeng Jiang

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2023 | Conference Paper |

FedSyL: Computation-Efficient Federated Synergy Learning on Heterogeneous IoT Devices

Hui Jiang;Min Liu;Sheng Sun;Yuwei Wang;Xiaobing Guo

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

Year: 2022 | Conference Paper |

Cited by: Papers (8)

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As a popular privacy-preserving model training technique, Federated Learning (FL) enables multiple end-devices to collaboratively train Deep Neural Network (DNN) models without exposing local privately-owned data. According to the FL paradigm, resource-constrained end-devices in IoT should perform model training which is computation-intensive, whereas the edge server occupied with powerful computa...Show More

FedSyL: Computation-Efficient Federated Synergy Learning on Heterogeneous IoT Devices

Hui Jiang;Min Liu;Sheng Sun;Yuwei Wang;Xiaobing Guo

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

Year: 2022 | Conference Paper |

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