Paving the Way for Distributed Artificial Intelligence over the Air

Guoqing Ma, Chuanting Zhang, Shuping Dang, Basem Shihada

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Distributed Artificial Intelligence (DAI) is 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. At the same time, the trained outputs, e.g., model parameters from multiple local clients, are sent back to a central server for aggregation. DAI is recently studied in conjunction with wireless communication networks to achieve better practicality, incorporating various random effects brought by wireless channels. However, because of wireless channels’ complex and case-dependent nature, a generic simulator for applying DAI in wireless communication networks is still lacking. To accelerate the development of DAI in wireless communication networks, we propose a generic system design in this paper and 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.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Open Journal of the Communications Society
StatePublished - Jul 7 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14


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