  Federated learning (FL) is a kind of distributed machine learning framework,
where the global model is generated on the centralized aggregation server based
on the parameters of local models, addressing concerns about privacy leakage
caused by the collection of local training data. With the growing computational
and communication capacities of edge and IoT devices, applying FL on
heterogeneous devices to train machine learning models is becoming a prevailing
trend. Nonetheless, the synchronous aggregation strategy in the classic FL
paradigm, particularly on heterogeneous devices, encounters limitations in
resource utilization due to the need to wait for slow devices before
aggregation in each training round. Furthermore, the uneven distribution of
data across devices (i.e. data heterogeneity) in real-world scenarios adversely
impacts the accuracy of the global model. Consequently, many asynchronous FL
(AFL) approaches have been introduced across various application contexts to
enhance efficiency, performance, privacy, and security. This survey
comprehensively analyzes and summarizes existing AFL variations using a novel
classification scheme, including device heterogeneity, data heterogeneity,
privacy, and security on heterogeneous devices, as well as applications on
heterogeneous devices. Finally, this survey reveals rising challenges and
presents potentially promising research directions in this under-investigated
domain.
