论文标题
基于联合蒸馏的物联网网络的室内本地化
Federated Distillation based Indoor Localization for IoT Networks
论文作者
论文摘要
最近提出了联合蒸馏（FD）范式作为联合学习（FL）的有前途替代品（FL），尤其是在通信资源有限的无线传感器网络中。但是，所有最先进的FD算法都是为了分类任务而设计的，并且对回归任务的关注更少。在这项工作中，我们提出了一个FD框架，该框架在回归学习问题上正确运作。之后，我们通过提出一个室内定位系统来提出用例实现，该系统与基于联邦学习（FL）的室内定位相比，表现出良好的权衡交流负载与准确性。有了我们提出的框架，我们将传输位的数量减少多达98％。此外，我们表明所提出的框架比FL更可扩展，因此更有可能应对无线网络的扩展。
Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithms are designed for only classification tasks and less attention has been given to regression tasks. In this work, we propose an FD framework that properly operates on regression learning problems. Afterwards, we present a use-case implementation by proposing an indoor localization system that shows a good trade-off communication load vs. accuracy compared to federated learning (FL) based indoor localization. With our proposed framework, we reduce the number of transmitted bits by up to 98%. Moreover, we show that the proposed framework is much more scalable than FL, thus more likely to cope with the expansion of wireless networks.
