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.

个性化联邦学习中的数据异质性是一个重要问题，目前的方法未能根据每个本地客户端的数据特征定制合作方式，导致聚合结果不理想。为解决这一问题，我们提出了一种基于算法展开的个性化联邦学习框架 Learn2pFed，使每个客户端能够自适应地选择其本地模型参数中应参与协作训练的部分。Learn2pFed 的关键创新之处在于通过算法展开方法，将每个本地模型参数的参与程度作为可学习参数进行优化。实验证明 Learn2pFed 在回归、预测和图像分类等多个任务中显著优于以往的个性化联邦学习方法。