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The development of applications based on artificial intelligence andimplemented over wireless networks is increasingly rapidly and is expected togrow dramatically in the future. The resulting demand for the aggregation oflarge amounts of data has caused serious communication bottlenecks in wirelessnetworks and particularly at the network edge. Over-the-air federated learning(OTA-FL), leveraging the superposition feature of multi-access channels (MACs),enables users at the network edge to share spectrum resources and achievesefficient and low-latency global model aggregation. This paper provides aholistic review of progress in OTA-FL and points to potential future researchdirections. Specifically, we classify OTA-FL from the perspective of systemsettings, including single-antenna OTA-FL, multi-antenna OTA-FL, and OTA-FLwith the aid of the emerging reconfigurable intelligent surface (RIS)technology, and the contributions of existing works in these areas aresummarized. Moreover, we discuss the trust, security and privacy aspects ofOTA-FL, and highlight concerns arising from security and privacy. Finally,challenges and potential research directions are discussed to promote thefuture development of OTA-FL in terms of improving system performance,reliability, and trustworthiness. Specifical challenges to be addressed includemodel distortion under channel fading, the ineffective OTA aggregation of localmodels trained on substantially unbalanced data, and the limited accessibilityand verifiability of individual local models.

Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions