ShanghaiTech University Knowledge Management System
| Fisher-Robust Information Bottleneck for Task-Oriented Communication with Noisy Data | |
| 2024-10-26 | |
| 会议录名称 | 2024 16TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP)
 |
| 发表状态 | 已发表 |
| DOI | 10.1109/WCSP62071.2024.10827769 |
| 摘要 | In this paper, we consider task-oriented commu-nication for edge AI inference, where the edge device extracts features from input data with potential noise and then transmits the encoded representations to the edge server over the physical channel for inference tasks. However, it remains an open problem to balance the tradeoff between inference accuracy and robustness in the presence of both data noise and channel noise. To address this issue, we propose a robust encoding framework for task-oriented communication, named Dual-Robust Encoding (DRE), where a robust encoding framework is developed, named Fisher-Robust Information Bottleneck (FR-IB), to balance the tradeoff between relevance and robustness. We derive a tractable variational upper bound of the FR-IB objective function using the variational approximation to overcome the computational intractability of mutual information. Experiments on image classification tasks demonstrate that DRE outperforms baseline methods, showing improved robustness to data and channel variations. |
| 会议地点 | Hefei, China |
| 会议日期 | 24-26 Oct. 2024 |
| URL | 查看原文 |
| 来源库 | IEEE |
| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/474150 |
| 专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_吴幼龙组 信息科学与技术学院_PI研究组_石远明组 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_文鼎柱组 |
| 作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Beijing Yizhuang Smart City Institute Group Co., Ltd, Beijing, China 3.School of Informatics, Xiamen University, Xiamen, China |
| 第一作者单位 | 信息科学与技术学院 |
| 第一作者的第一单位 | 信息科学与技术学院 |
| 推荐引用方式 GB/T 7714 | Jielin Zhu,Youlong Wu,Dingzhu Wen,et al. Fisher-Robust Information Bottleneck for Task-Oriented Communication with Noisy Data[C],2024. |
| 条目包含的文件 | ||||||
| 条目无相关文件。 | ||||||
修改评论
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。