Federated learning (FL) is experiencing a fast booming with the wave of distributed machine learning. In the FL paradigm, the global model is aggregated on the centralized aggregation server according to the parameters of local models instead of local training data, mitigating privacy leakage caused by the collection of sensitive information. With the increased computing and communication capabilities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models becomes a trend. The synchronous aggregation strategy in the classic FL paradigm cannot effectively use the limited resource, especially on heterogeneous devices, due to its waiting for straggler devices before aggregation in each training round. Furthermore, the disparity of data spread on devices (i.e. data heterogeneity) in real-world scenarios downgrades the accuracy of models. As a result, many asynchronous FL (AFL) paradigms are presented in various application scenarios to improve efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing variants of AFL according to a novel classification mechanism, including device heterogeneity, data heterogeneity, privacy and security on heterogeneous devices, and applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated field.

翻译：联邦学习正随着分布式机器学习的浪潮迅猛发展。在联邦学习范式中，全局模型在中央聚合服务器上根据本地模型的参数进行聚合，而非依赖本地训练数据，从而减轻了敏感信息收集所导致的隐私泄露问题。随着边缘设备与物联网设备计算与通信能力的提升，将联邦学习应用于异构设备以训练机器学习模型已成为趋势。经典联邦学习范式中的同步聚合策略由于需要在每轮训练中等待掉队设备，无法有效利用有限资源，尤其在异构设备上表现尤为突出。此外，现实场景中设备间数据分布的不均衡性（即数据异质性）会降低模型精度。为此，针对不同应用场景提出了多种异步联邦学习范式，以提升效率、性能、隐私性与安全性。本综述根据一种新颖的分类机制，全面分析与总结了现有异步联邦学习的变体，涵盖设备异质性、数据异质性、异构设备上的隐私性与安全性，以及异构设备上的应用。最后，本综述揭示了该尚未充分研究领域中的新兴挑战，并指出了潜在有价值的研究方向。