Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866918079691227136 |
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| author | Erak, Omar Alhussein, Omar Tong, Wen |
| author_facet | Erak, Omar Alhussein, Omar Tong, Wen |
| contents | Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission in next-generation networks, where only information relevant to a specific task is communicated. This is particularly important in 6G-enabled Internet of Things (6G-IoT) scenarios, where bandwidth constraints, latency requirements, and data privacy are critical. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant information, leading to privacy concerns and suboptimal performance. To address this, we propose an information-bottleneck inspired method, named CLAD (contrastive learning and adversarial disentanglement). CLAD utilizes contrastive learning to effectively capture task-relevant features while employing adversarial disentanglement to discard task-irrelevant information. Additionally, due to the absence of reliable and reproducible methods to quantify the minimality of encoded feature vectors, we introduce the Information Retention Index (IRI), a comparative metric used as a proxy for the mutual information between the encoded features and the input. The IRI reflects how minimal and informative the representation is, making it highly relevant for privacy-preserving and bandwidth-efficient 6G-IoT systems. Extensive experiments demonstrate that CLAD outperforms state-of-the-art baselines in terms of semantic extraction, task performance, privacy preservation, and IRI, making it a promising building block for responsible, efficient and trustworthy 6G-IoT services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22784 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication Erak, Omar Alhussein, Omar Tong, Wen Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Information Theory Image and Video Processing Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission in next-generation networks, where only information relevant to a specific task is communicated. This is particularly important in 6G-enabled Internet of Things (6G-IoT) scenarios, where bandwidth constraints, latency requirements, and data privacy are critical. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant information, leading to privacy concerns and suboptimal performance. To address this, we propose an information-bottleneck inspired method, named CLAD (contrastive learning and adversarial disentanglement). CLAD utilizes contrastive learning to effectively capture task-relevant features while employing adversarial disentanglement to discard task-irrelevant information. Additionally, due to the absence of reliable and reproducible methods to quantify the minimality of encoded feature vectors, we introduce the Information Retention Index (IRI), a comparative metric used as a proxy for the mutual information between the encoded features and the input. The IRI reflects how minimal and informative the representation is, making it highly relevant for privacy-preserving and bandwidth-efficient 6G-IoT systems. Extensive experiments demonstrate that CLAD outperforms state-of-the-art baselines in terms of semantic extraction, task performance, privacy preservation, and IRI, making it a promising building block for responsible, efficient and trustworthy 6G-IoT services. |
| title | Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Information Theory Image and Video Processing |
| url | https://arxiv.org/abs/2410.22784 |