Because studied the joint resource allocation and edge device selection of the limited radio spectrum resource and increasing privacy to enhance learning performance. Both studies adopted the concerns, gathering geographically distributed data from a orthogonal multiple access (OMA) scheme, where the number large number of edge devices into a cloud server to enable of edge devices that can participate in each communication cloud artificial intelligence (AI) may not be practical. To this round is restricted by the number of available time/frequency end, edge AI has recently been envisioned as a promising AI resource blocks. The limited radio resource turns out to be the paradigm [1]. Unlike cloud AI that relies on a cloud server to main performance bottleneck of wireless FL. Fortunately, overthe-air conduct centralized training, edge AI exploits the computing computation (AirComp), as a non-orthogonal multiple power of multiple edge devices to perform model training access scheme, allows concurrent transmissions over the same with their own local data in a distributed manner. Federated radio channel to enable low-latency and spectrum-efficient learning (FL) [2], as a representative edge AI framework, wireless data aggregation [13]-[15], thereby mitigating the enables multiple edge devices to collaboratively train a shared communication bottleneck [16]. Motivated by this observation, model without exchanging their local data, which effectively various AirComp-assisted FL algorithms were proposed in alleviates the communication burden and privacy concerns.