Personalized federated learning becomes a hot research topic that can learn a
personalized learning model for each client. Existing personalized federated
learning models prefer to aggregate similar clients with similar data
distribution to improve the performance of learning models. However,
similaritybased personalized federated learning methods may exacerbate the
class imbalanced problem. In this paper, we propose a novel Dynamic
Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the
class imbalanced problem during federated learning. Specifically, we build an
affinity metric from a complementary perspective to guide which clients should
be aggregated. Then we design a dynamic aggregation strategy to dynamically
aggregate clients based on the affinity metric in each round to reduce the
class imbalanced risk. Extensive experiments show that the proposed DA-PFL
model can significantly improve the accuracy of each client in three real-world
datasets with state-of-the-art comparison methods.

我们提出了一种新颖的动态亲和力个性化联邦学习模型（DA-PFL），以减轻联邦学习过程中的类不平衡问题。通过从互补的角度构建亲和度度量，并设计动态聚合策略来减少类不平衡风险，DA-PFL 模型可以显著提高三个真实世界数据集中每个客户的准确性。