Paving the Way for Distributed Artificial Intelligence over the Air

TL;DR

This paper introduces a generic system and simulator for distributed artificial intelligence over wireless networks, addressing the complex effects of wireless channels to enhance development and deployment.

Contribution

It proposes a flexible system design and a configurable simulator for DAI in wireless environments, enabling better analysis and implementation.

Findings

The simulator accurately models wireless channel effects on DAI.

The system design improves scalability and efficiency.

Experiments confirm the effectiveness of the approach.

Abstract

Distributed Artificial Intelligence (DAI) is regarded as one of the most promising techniques to provide intelligent services under strict privacy protection regulations for multiple clients. By applying DAI, training on raw data is carried out locally, while the trained outputs, e.g., model parameters, from multiple local clients, are sent back to a central server for aggregation. Recently, for achieving better practicality, DAI is studied in conjunction with wireless communication networks, incorporating various random effects brought by wireless channels. However, because of the complex and case-dependent nature of wireless channels, a generic simulator for applying DAI in wireless communication networks is still lacking. To accelerate the development of DAI applied in wireless communication networks, we propose a generic system design in this paper as well as an associated simulator that can be set according to wireless channels and system-level configurations. Details of the system design and analysis of the impacts of wireless environments are provided to facilitate further implementations and updates. We employ a series of experiments to verify the effectiveness and efficiency of the proposed system design and reveal its superior scalability.