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arXiv · 2309.08961

UNIDEAL: Curriculum Knowledge Distillation Federated Learning

Yuwen Yang·Chang Liu·Xun Cai·Suizhi Huang·Hongtao Lu·Yue Ding
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Abstract

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.

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Yuwen Yang, Chang Liu, Xun Cai, Suizhi Huang, Hongtao Lu, Yue Ding. 2023-09-16. UNIDEAL: Curriculum Knowledge Distillation Federated Learning. https://doi.org/10.1109/icassp48485.2024.10447769

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