In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources. This diversity leads to significant heterogeneity, which increases the complexity of model training. In this paper, we first outline the basic concepts of heterogeneous federated learning and summarize the research challenges in federated learning in terms of five aspects: data, model, task, device, and communication. In addition, we explore how existing state-of-the-art approaches cope with the heterogeneity of federated learning, and categorize and review these approaches at three different levels: data-level, model-level, and architecture-level. Subsequently, the paper extensively discusses privacy-preserving strategies in heterogeneous federated learning environments. Finally, the paper discusses current open issues and directions for future research, aiming to promote the further development of heterogeneous federated learning.

翻译：在异构联邦学习（FL）领域中，核心挑战在于高效协作训练跨多个客户端（这些客户端具有不同数据分布、模型结构、任务目标、计算能力和通信资源）的模型。这种多样性导致显著的异构性，从而增加了模型训练的复杂性。本文首先概述了异构联邦学习的基本概念，并从数据、模型、任务、设备和通信五个方面总结了联邦学习的研究挑战。此外，我们探讨了现有最先进方法如何应对联邦学习的异构性，并在数据级、模型级和架构级三个不同层面上对这些方法进行分类和综述。随后，本文广泛讨论了异构联邦学习环境中的隐私保护策略。最后，本文探讨了当前开放性问题及未来研究方向，旨在促进异构联邦学习的进一步发展。