Adaptive Task-Oriented Communication with Fairness Guarantees

Learning-based joint source-channel coding (JSCC) is widely used in task-oriented communication, which aims to extract and transmit only task-relevant information to improve communication efficiency. However, the learning-empowered algorithms in task-oriented communication may bring potential bias towards sensitive groups, and the adaptability to dynamic channel conditions still remains a challenge. To address these issues, we propose a task-oriented communication scheme that achieves efficient encoding and inference while preserving group fairness. Our approach leverages an information bottleneckbased framework that maximizes the task utility information while limiting the dependence of the inference result on the sensitive attribute and adopts a hypernetwork-parametrization mechanism to adapt to varying channel conditions. We also provide a theoretical bound for fairness guarantee and design a noise injection module to control the fairness-utility tradeoff. Experiments on benchmark datasets demonstrate the superiority of our framework in achieving a fairness-utility tradeoff and the adaptability to channel variations.