Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, where clients possess data from different domains or distributions, remain a challenging problem due to the inherent heterogeneity. In this paper, we present UNIDEAL, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios and heterogeneous model architectures. The proposed method introduces Adjustable Teacher-Student Mutual Evaluation Curriculum Learning, which significantly enhances the effectiveness of knowledge distillation in FL settings. We conduct extensive experiments on various datasets, comparing UNIDEAL with state-of-the-art baselines. Our results demonstrate that UNIDEAL achieves superior performance in terms of both model accuracy and communication efficiency. Additionally, we provide a convergence analysis of the algorithm, showing a convergence rate of O(1/T) under non-convex conditions.

翻译：联邦学习（FL）已成为一种有前景的方法，能够在保护数据隐私的同时实现多个客户端之间的协作学习。然而，跨领域FL任务中，客户端持有的数据来自不同领域或分布，由于固有的异质性，这一问题仍然具有挑战性。本文提出UNIDEAL，一种专为应对跨领域场景和异构模型架构挑战而设计的新型FL算法。该方法引入了可调节师生互评课程学习，显著提升了FL设置中知识蒸馏的有效性。我们在多个数据集上进行了广泛实验，将UNIDEAL与最先进的基线方法进行比较。结果表明，UNIDEAL在模型准确性和通信效率方面均实现了优越性能。此外，我们提供了算法的收敛性分析，表明在非凸条件下收敛率为O(1/T)。