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Distributed Artificial Intelligence (DAI) is regarded as one of the mostpromising techniques to provide intelligent services under strict privacyprotection regulations for multiple clients. By applying DAI, training on rawdata 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 withwireless communication networks, incorporating various random effects broughtby wireless channels. However, because of the complex and case-dependent natureof wireless channels, a generic simulator for applying DAI in wirelesscommunication networks is still lacking. To accelerate the development of DAIapplied in wireless communication networks, we propose a generic system designin this paper as well as an associated simulator that can be set according towireless channels and system-level configurations. Details of the system designand analysis of the impacts of wireless environments are provided to facilitatefurther implementations and updates. We employ a series of experiments toverify the effectiveness and efficiency of the proposed system design andreveal its superior scalability.

Paving the Way for Distributed Artificial Intelligence over the Air