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：本文聚焦于解决联邦学习中一个实际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。针对该问题，我们提出了一种新颖框架pFedHR，通过利用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转化为服务器端的模型匹配优化任务。此外，pFedHR能够在无需人工干预的情况下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，我们所提出的异构模型重组技术可有效缓解使用与客户端数据分布不同的公共数据所带来的负面影响。实验结果表明，在独立同分布和非独立同分布两种设定下，pFedHR在三个数据集上的表现均优于基线方法。同时，pFedHR能够有效降低使用不同公共数据带来的负面影响，并以自动化方式动态生成多样化的个性化模型。