In-Network Learning: Distributed Training and Inference in Networks

TL;DR

This paper introduces a distributed learning algorithm and architecture that enable training and inference across wireless networks, addressing data and processing distribution challenges, with demonstrated benefits over existing methods.

Contribution

It proposes a novel in-network learning framework that facilitates distributed training and inference in wireless networks, including analysis, design criteria, and implementation insights.

Findings

Inference propagates and fuses across the network

Bandwidth requirements are analyzed and optimized

Experiments show benefits over state-of-the-art techniques

Abstract

It is widely perceived that leveraging the success of modern machine learning techniques to mobile devices and wireless networks has the potential of enabling important new services. This, however, poses significant challenges, essentially due to that both data and processing power are highly distributed in a wireless network. In this paper, we develop a learning algorithm and an architecture that make use of multiple data streams and processing units, not only during the training phase but also during the inference phase. In particular, the analysis reveals how inference propagates and fuses across a network. We study the design criterion of our proposed method and its bandwidth requirements. Also, we discuss implementation aspects using neural networks in typical wireless radio access; and provide experiments that illustrate benefits over state-of-the-art techniques.