Fairness-Aware Medical Image Analysis for Consumer Electronics Under Imbalanced Data

Consumer health electronic systems increasingly rely on the seamless integration of sensing, communication, computing, and control to deliver intelligent, adaptive, and context-aware services. However, under imbalanced environments and heterogeneous network conditions, these integrated systems often exhibit performance disparities that affect reliability and user trust. In this paper, we propose a novel framework for fair and robust integration of sensing, communication, computing, and control in consumer health electronics. Our approach adopts a three-fold strategy: (i) feature disentanglement to separate environment-dependent signals from task-relevant features, reducing bias in sensing and decision-making; (ii) a fairness-aware optimization scheme that balances resource allocation across diverse devices and network conditions, ensuring equitable performance; and (iii) loss landscape flattening through sharpness-aware optimization to enhance robustness against distribution shifts in communication channels and computing platforms. Extensive experiments on representative IoT and edge-computing benchmarks demonstrate that our method significantly improves both fairness and stability across heterogeneous consumer health scenarios, providing a promising direction for equitable and reliable smart device ecosystems.