论文标题
基于信息瓶颈的边缘学习的目标沟通
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck
论文作者
论文摘要
每当发生实现目标的通信时，要传输要传输的源数据的有效方法是使用一个编码规则，该规则允许接收者满足目标的要求。可以利用信息瓶颈（IB）原则来确定有关目标的相关信息的正式方法。在本文中，我们根据IB和随机优化的组合提出了一个面向目标的通信系统。 IB原理用于设计编码器，以便在代表性复杂性和相关性相关性之间找到最佳平衡。然后使用随机优化来调整IB的参数，以找到有效的通信和计算资源资源分配。我们的目标是最大程度地减少在动态场景中应用于接收到的数据的平均服务延迟和学习任务的平均服务延迟和准确性的平均能源消耗。数值结果评估了两种情况下提出的策略的性能：从高斯随机变量中回归，我们可以利用封闭形式的解决方案，并使用深层神经网络进行图像分类，并在传播和接收方之间进行自适应网络分裂。
Whenever communication takes place to fulfil a goal, an effective way to encode the source data to be transmitted is to use an encoding rule that allows the receiver to meet the requirements of the goal. A formal way to identify the relevant information with respect to a goal can be obtained exploiting the information bottleneck (IB) principle. In this paper, we propose a goal-oriented communication system, based on the combination of IB and stochastic optimization. The IB principle is used to design the encoder in order to find an optimal balance between representation complexity and relevance of the encoded data with respect to the goal. Stochastic optimization is then used to adapt the parameters of the IB to find an efficient resource allocation of communication and computation resources. Our goal is to minimize the average energy consumption under constraints on average service delay and accuracy of the learning task applied to the received data in a dynamic scenario. Numerical results assess the performance of the proposed strategy in two cases: regression from Gaussian random variables, where we can exploit closed-form solutions, and image classification using deep neural networks, with adaptive network splitting between transmit and receive sides.
