Federated learning (FL) has drawn increasing attention owing to its potential use in large-scale industrial applications. Existing federated learning works mainly focus on model homogeneous settings. However, practical federated learning typically faces the heterogeneity of data distributions, model architectures, network environments, and hardware devices among participant clients. Heterogeneous Federated Learning (HFL) is much more challenging, and corresponding solutions are diverse and complex. Therefore, a systematic survey on this topic about the research challenges and state-of-the-art is essential. In this survey, we firstly summarize the various research challenges in HFL from five aspects: statistical heterogeneity, model heterogeneity, communication heterogeneity, device heterogeneity, and additional challenges. In addition, recent advances in HFL are reviewed and a new taxonomy of existing HFL methods is proposed with an in-depth analysis of their pros and cons. We classify existing methods from three different levels according to the HFL procedure: data-level, model-level, and server-level. Finally, several critical and promising future research directions in HFL are discussed, which may facilitate further developments in this field. A periodically updated collection on HFL is available at https://github.com/marswhu/HFL_Survey.

翻译：联邦学习（FL）因其在大规模工业应用中的潜力而日益受到关注。现有联邦学习研究主要聚焦于模型同构场景。然而，实际联邦学习通常面临参与客户间数据分布、模型架构、网络环境及硬件设备的异构性。异构联邦学习（HFL）更具挑战性，且相应解决方案多样复杂。因此，对该主题的研究挑战与前沿进展进行系统性综述至关重要。本文首先从五个维度总结HFL中的各类研究挑战：统计异构性、模型异构性、通信异构性、设备异构性及其他挑战。此外，我们综述了HFL的最新进展，提出了一种针对现有HFL方法的新分类体系，并深入分析了各类方法的优缺点。根据HFL流程，我们从数据级、模型级和服务器级三个不同层面对现有方法进行了分类。最后，探讨了HFL中几个关键且富有前景的未来研究方向，以推动该领域的进一步发展。相关HFL资料的定期更新内容可见于https://github.com/marswhu/HFL_Survey。