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.

翻译：联邦学习（FL）是一种分布式机器学习框架，其核心思想是在中央聚合服务器上基于局部模型的参数生成全局模型，从而解决因收集局部训练数据而引发的隐私泄露问题。随着边缘和物联网设备的计算与通信能力不断提升，在异构设备上应用联邦学习训练机器学习模型已成为主流趋势。然而，经典联邦学习范式中的同步聚合策略——尤其在异构设备场景下——因每个训练回合需等待慢速设备完成聚合，导致资源利用率受限。此外，真实场景中数据在各设备间的不均匀分布（即数据异构性）会损害全局模型的精度。为此，研究者针对不同应用场景提出了多种异步联邦学习（AFL）方法，以提升效率、性能、隐私性和安全性。本综述通过新颖的分类体系，全面分析与总结了现有异步联邦学习的变体，涵盖设备异构性、数据异构性、隐私与安全保护，以及异构设备上的实际应用。最后，本文揭示了这一尚待深入探索领域中的新兴挑战，并提出了具有前景的研究方向。