UNIDEAL: Curriculum Knowledge Distillation Federated Learning

• URL: http://arxiv.org/abs/2309.08961v1
• Date: Sat, 16 Sep 2023 11:30:29 GMT
• Title: UNIDEAL: Curriculum Knowledge Distillation Federated Learning
• Authors: Yuwen Yang, Chang Liu, Xun Cai, Suizhi Huang, Hongtao Lu, Yue Ding
• Abstract summary: Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients. In this paper, we present UNI, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios. Our results demonstrate that UNI achieves superior performance in terms of both model accuracy and communication efficiency.
• Score: 17.817181326740698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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