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Fairness-Aware Joint Source-Channel Coding for Robust Task-Oriented Communication
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DOI：10.1109/JSAC.2025.3638730.png)
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Abstract
En 中文
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 lead to information leakage on sensitive attributes and cause discrimination towards specific groups, resulting in fairness issues in social equity. Meanwhile, directly adopting fair representation learning techniques in the source encoder of communication systems poses significant challenges: First, the favorable fairness-utility tradeoff in the encoded feature representations would be deteriorated by channel noise and dynamic variations. Second, the inherent separation of source and channel design precludes the efficiency offered by JSCC for end-to-end transmission. To address these issues, we propose a task-oriented JSCC communication scheme, namely Fair-RIB, that achieves efficient encoding and inference while preserving group fairness. Our approach leverages an information bottleneck-based framework that maximizes the task utility information while limiting the sensitive information leakage to ensure fairness, and adopts a hypernetwork-parametrization mechanism to adapt to varying channel conditions. We also provide theoretical bounds for fairness guarantees by fully exploiting the characteristics of the channel noise, and introduce a selective noise injection mechanism to better manage the fairness-utility tradeoff. To overcome the intractability of the high-dimensional mutual information terms, we adopt variational approximations to derive a tractable upper bound for objective optimization. Experiments on benchmark tabular and image datasets demonstrate the superiority of our framework in achieving a fairness-utility tradeoff and the adaptability to channel variations.
Keywords:
Task-oriented communication
joint source-channel coding
group fairness
fairness-utility tradeoff
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