  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.
