# Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication

arXiv: https://arxiv.org/abs/2410.22784 · HTML: https://mlsanity.com/paper/2410.22784/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Disentangling by Factorising](https://mlsanity.com/paper/1802.05983/) (2018)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Understanding the Limitations of Variational Mutual Information Estimators](https://mlsanity.com/paper/1910.06222/) (2019)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [The Conditional Entropy Bottleneck](https://mlsanity.com/paper/2002.05379/) (2020)
- [Learning Disentangled Representations with Semi-Supervised Deep Generative Models](https://mlsanity.com/paper/1706.00400/) (2017)
- [Learning Disentangled Representations via Mutual Information Estimation](https://mlsanity.com/paper/1912.03915/) (2019)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation](https://mlsanity.com/paper/2209.10382/) (2022)
- [Disentangled Information Bottleneck](https://mlsanity.com/paper/2012.07372/) (2020)
- [Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2304.01914/) (2023)

## Built on by (most cited first, 3 of 3)

- [Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edge](https://mlsanity.com/paper/2509.09955/) (2025)
- [Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication](https://mlsanity.com/paper/2509.09168/) (2025)
- [Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack](https://mlsanity.com/paper/2506.19886/) (2025)

## Abstract (arXiv)

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.