DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
Xu Yang Jiyuan Feng Songyue Guo Ye Wang Ye Ding Binxing Fang Qing Liao
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.