论文标题
与模型隐私的无线合奏推理
Over-the-Air Ensemble Inference with Model Privacy
论文作者
论文摘要
我们考虑在无线边缘的分布推理，其中有多种模型集合的客户并行查询每个模型，每个客户均在本地数据集上独立培训，以便对新样本做出准确的决定。除了最大化推理精度外，我们还希望最大化本地模型的隐私。我们利用空气的叠加属性来实现带宽有效的集合推理方法。我们介绍了不同的台外合奏方法，并表明这些方案的性能明显优于其正交对应物，同时使用较少的资源并提供隐私保证。我们还提供了实验结果，以验证拟议的无线推理方法的好处，其源代码在Github上公开共享。
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample. In addition to maximizing inference accuracy, we also want to maximize the privacy of local models. We exploit the superposition property of the air to implement bandwidth-efficient ensemble inference methods. We introduce different over-the-air ensemble methods and show that these schemes perform significantly better than their orthogonal counterparts, while using less resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed over-the-air inference approach, whose source code is shared publicly on Github.
