Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate either full model parameters or predefined partial parameters in personalized federated learning. They fail to customize the collaboration manner according to each local client's data characteristics, causing unpleasant aggregation results. To address this essential issue, we propose $\textit{Learn2pFed}$, a novel algorithm-unrolling-based personalized federated learning framework, enabling each client to adaptively select which part of its local model parameters should participate in collaborative training. The key novelty of the proposed $\textit{Learn2pFed}$ is to optimize each local model parameter's degree of participant in collaboration as learnable parameters via algorithm unrolling methods. This approach brings two benefits: 1) mathmatically determining the participation degree of local model parameters in the federated collaboration, and 2) obtaining more stable and improved solutions. Extensive experiments on various tasks, including regression, forecasting, and image classification, demonstrate that $\textit{Learn2pFed}$ significantly outperforms previous personalized federated learning methods.

翻译：个性化联邦学习旨在解决联邦学习中各本地客户端之间的数据异质性。然而，现有方法在个性化联邦学习中盲目地整合全部模型参数或预定义的部分参数，未能根据每个本地客户端的数据特征定制协作方式，导致聚合结果不佳。为解决这一关键问题，我们提出了一种基于算法展开的个性化联邦学习框架$\textit{Learn2pFed}$，使每个客户端能够自适应地选择其本地模型的哪些部分参与协作训练。$\textit{Learn2pFed}$的核心创新在于将每个本地模型参数在协作中的参与程度作为可学习参数，通过算法展开方法进行优化。这一方法带来两个优势：1）数学上确定本地模型参数在联邦协作中的参与程度；2）获得更稳定且更优的解决方案。在包含回归、预测和图像分类等多种任务的广泛实验中，$\textit{Learn2pFed}$显著优于现有的个性化联邦学习方法。