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.

本文提出了一种新颖的框架pFedHR，用于解决联邦学习中模型异质性的实际挑战，通过利用异构模型重组实现个性化的联邦学习，在服务器端将异构模型个性化问题看作模型匹配优化任务，自动动态生成多样、具有信息量的个性化模型，减轻了使用具有不同分布的公共数据对客户数据的不利影响，并在实验结果中显示pFedHR在三个数据集上在IID和非IID设置下优于基线方法。

通过异构模型重组实现个性化联邦学习