In-Network Learning: Distributed Training and Inference in Networks
This addresses the problem of enabling machine learning services on mobile devices and wireless networks, which is incremental as it builds on existing techniques for distributed settings.
The paper tackles the challenge of distributed data and processing in wireless networks by developing a learning algorithm and architecture for both training and inference phases, demonstrating benefits over state-of-the-art techniques through experiments.
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.