In-Network Learning via Over-the-Air Computation in Internet-of-Things

This work proposes a novel in-network learning framework for distributed inference in internet-of-things (IoT), where low-cost sensor devices may collaborate to form a deep neural network over wireless multihop links. The devices are grouped into multiple layers, including a source layer, several relay layers, and a destination layer. Each individual device consists of only a small neural sub-network, but their aggregate is capable of performing complex inference tasks as local observations are gradually forwarded in a layer-by-layer fashion to the destination. By leveraging the superposition property of the multiple access channel, we propose the use of over-the-air computation to efficiently perform the linear combining operations required between neural network layers over a limited number of time slots. Two approaches are proposed for the design of the transmit and receive filters to enable over-the-air computation. In the first approach, the filters are designed to approximate the weight parameters of a centralized neural network based on the minimum mean squared error (MMSE) criterion. In the second approach, the filters are directly obtained by end-to-end training over varying channel realizations. Simulation results show that our proposed in-network learning framework is able to achieve an inference accuracy close to that of the centralized model as the signal-to-noise ratio and the number of time slots increases.