  Personalized Federated Learning (PFL) relies on collective data knowledge to
build customized models. However, non-IID data between clients poses
significant challenges, as collaborating with clients who have diverse data
distributions can harm local model performance, especially with limited
training data. To address this issue, we propose FedACS, a new PFL algorithm
with an Attention-based Client Selection mechanism. FedACS integrates an
attention mechanism to enhance collaboration among clients with similar data
distributions and mitigate the data scarcity issue. It prioritizes and
allocates resources based on data similarity. We further establish the
theoretical convergence behavior of FedACS. Experiments on CIFAR10 and FMNIST
validate FedACS's superiority, showcasing its potential to advance personalized
federated learning. By tackling non-IID data challenges and data scarcity,
FedACS offers promising advances in the field of personalized federated
learning.
