Gang Wang

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每年的科研成果

与联合国可持续发展目标相关的专业知识

2015 年，联合国成员国同意 17 项可持续发展目标 (SDG)，以消除贫困、保护地球并确保全人类的繁荣。此人的工作有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

研究主题

深入其中 Gang Wang 为活跃的研究主题。这些主题标签来自此人的成果。它们共同形成唯一的指纹。

1 相似学者

AdaDQ-KD: An Adaptive Dithering Quantization with Knowledge Distillation in Privacy-Preserving Federated Learning
Wang, G., Qi, Q., Yu, H., Han, R., Bai, L. & Choi, J., 2026, (已接受/待刊) 在: IEEE Transactions on Vehicular Technology.
科研成果: 期刊稿件 › 文章 › 同行评审
Toward Trusted 6G Mobile Edge Computing: A Secure Batch Large Language Models Deployment Framework
Sun, Y., Liu, J., Xiong, G., Song, Q., Liu, J., Wang, G. & Wang, R., 2026, 在: IEEE Transactions on Mobile Computing. 25, 3, 页码 3328-3346 19 页码
科研成果: 期刊稿件 › 文章 › 同行评审
2 !!Link opens in a new tab 引用（Scopus）
Multiple Aircraft Trajectory Prediction Based on Bayesian Social LSTM
Zhong, X., Cai, K., Wang, G. & Zhao, P., 2025, AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025. American Institute of Aeronautics and Astronautics Inc, AIAA, (AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025).
科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审
3 !!Link opens in a new tab 引用（Scopus）
Efficient Federated Learning via Joint Communication and Computation Optimization
Wang, G., Zhao, C., Qi, Q., Han, R., Bai, L. & Choi, J., 2024, 在: IEEE Transactions on Vehicular Technology. 73, 8, 页码 11580-11592 13 页码
科研成果: 期刊稿件 › 文章 › 同行评审

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9 !!Link opens in a new tab 引用（Scopus）
P2CEFL: Privacy-Preserving and Communication Efficient Federated Learning With Sparse Gradient and Dithering Quantization
Wang, G., Qi, Q., Han, R., Bai, L. & Choi, J., 2024, 在: IEEE Transactions on Mobile Computing. 23, 12, 页码 14722-14736 15 页码
科研成果: 期刊稿件 › 文章 › 同行评审
4 !!Link opens in a new tab 引用（Scopus）

王钢

个人简介

与联合国可持续发展目标相关的专业知识

研究主题

最近五年的合作关系和顶尖研究领域

查看详情

AdaDQ-KD: An Adaptive Dithering Quantization with Knowledge Distillation in Privacy-Preserving Federated Learning

Toward Trusted 6G Mobile Edge Computing: A Secure Batch Large Language Models Deployment Framework

Multiple Aircraft Trajectory Prediction Based on Bayesian Social LSTM

Efficient Federated Learning via Joint Communication and Computation Optimization

P2CEFL: Privacy-Preserving and Communication Efficient Federated Learning With Sparse Gradient and Dithering Quantization

王 钢

个人简介

与联合国可持续发展目标相关的专业知识

研究主题

最近五年的合作关系和顶尖研究领域

科研成果

AdaDQ-KD: An Adaptive Dithering Quantization with Knowledge Distillation in Privacy-Preserving Federated Learning

Toward Trusted 6G Mobile Edge Computing: A Secure Batch Large Language Models Deployment Framework

Multiple Aircraft Trajectory Prediction Based on Bayesian Social LSTM

Efficient Federated Learning via Joint Communication and Computation Optimization

P2CEFL: Privacy-Preserving and Communication Efficient Federated Learning With Sparse Gradient and Dithering Quantization

王钢