This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner.

翻译：本文聚焦于联邦学习中一个实用且具有挑战性的问题——模型异构性，即客户端拥有不同网络结构的模型。为解决该问题，我们提出了一种名为pFedHR的新型框架，该框架通过异构模型重组实现个性化联邦学习。具体而言，我们将异构模型个性化问题视为服务器端的一种模型匹配优化任务。此外，pFedHR能够以最小人工干预自动动态生成信息丰富且多样化的个性化候选方案。更进一步，我们提出的异构模型重组技术在一定程度上缓解了使用与客户端数据分布不同的公开数据所带来的负面影响。实验结果表明，在独立同分布与非独立同分布两种设置下，pFedHR在三个数据集上均优于基线方法。此外，pFedHR有效降低了使用不同公开数据带来的负面影响，并能自动动态生成多样化的个性化模型。