通过智能路由减少医疗图像推理能耗，同时保护隐私并提升诊断可靠性。
EcoFair: Trustworthy and Energy-Aware Routing for Privacy-Preserving Vertically Partitioned Medical Inference
- 仅传输模态嵌入，本地保留原始数据，保障隐私。
- 根据不确定性和风险评分动态启用高耗能图像编码器，降低边缘能耗30%以上。
- 适合资源受限的边缘医疗设备部署，尤其关注恶性病变识别的公平性。
隐私保护的医疗推理需兼顾数据本地化、诊断可靠性与部署效率。本文提出EcoFair，一种模拟垂直分区推理框架，用于皮肤科诊断：原始图像与表格数据保留在本地，仅传输模态特定嵌入以进行服务器端多模态融合。EcoFair引入轻量级优先路由机制，当本地不确定性或基于元数据的临床风险提示需额外计算时，才激活更重的图像编码器。路由决策结合预测不确定性、安全-危险概率差值，以及由患者年龄和病灶位置推导的表格神经符号风险评分。在三个皮肤科基准上的实验表明，EcoFair在典型模型组合下可显著降低边缘推理能耗，同时保持分类性能竞争力。结果还显示，选择性路由能在不修改全局训练目标的前提下，改善代表性场景中对亚组恶性病例的表现。这些发现使EcoFair成为边缘部署约束下可信且节能的隐私保护医疗推理实用框架。
原文摘要 · Abstract (English)
Privacy-preserving medical inference must balance data locality, diagnostic reliability, and deployment efficiency. This paper presents EcoFair, a simulated vertically partitioned inference framework for dermatological diagnosis in which raw image and tabular data remain local and only modality-specific embeddings are transmitted for server-side multimodal fusion. EcoFair introduces a lightweight-first routing mechanism that selectively activates a heavier image encoder when local uncertainty or metadata-derived clinical risk indicates that additional computation is warranted. The routing decision combines predictive uncertainty, a safe--danger probability gap, and a tabular neurosymbolic risk score derived from patient age and lesion localisation. Experiments on three dermatology benchmarks show that EcoFair can substantially reduce edge-side inference energy in representative model pairings while remaining competitive in classification performance. The results further indicate that selective routing can improve subgroup-sensitive malignant-case behaviour in representative settings without modifying the global training objective. These findings position EcoFair as a practical framework for privacy-preserving and energy-aware medical inference under edge deployment constraints.
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