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.

UNIDEAL 是一种针对跨领域场景和异构模型架构的新型 FL 算法，引入了可调节的师生互评课程学习来显著增强 FL 设置中的知识蒸馏效果，在各个数据集上的实验结果表明，UNIDEAL 在模型准确性和通信效率方面均表现出优异的性能，同时还提供了算法的收敛分析，展示了在非凸条件下的 O (1/T) 收敛速度。