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Whenever communication takes place to fulfil a goal, an effective way toencode the source data to be transmitted is to use an encoding rule that allowsthe receiver to meet the requirements of the goal. A formal way to identify therelevant information with respect to a goal can be obtained exploiting theinformation bottleneck (IB) principle. In this paper, we propose agoal-oriented communication system, based on the combination of IB andstochastic optimization. The IB principle is used to design the encoder inorder to find an optimal balance between representation complexity andrelevance of the encoded data with respect to the goal. Stochastic optimizationis then used to adapt the parameters of the IB to find an efficient resourceallocation of communication and computation resources. Our goal is to minimizethe average energy consumption under constraints on average service delay andaccuracy of the learning task applied to the received data in a dynamicscenario. Numerical results assess the performance of the proposed strategy intwo cases: regression from Gaussian random variables, where we can exploitclosed-form solutions, and image classification using deep neural networks,with adaptive network splitting between transmit and receive sides.

Goal-Oriented Communication for Edge Learning based on the Information Bottleneck