Cross-Fusion Rule for Personalized Federated Learning
This addresses data heterogeneity and overfitting in federated learning for personalized applications, but it appears incremental as it builds on existing fusion strategies.
The paper tackles performance challenges in personalized federated learning due to data scarcity and heterogeneity by proposing a multi-layer multi-fusion strategy framework, which improves efficiency and alleviates overfitting, showing superiority over state-of-the-art methods in experiments.
Data scarcity and heterogeneity pose significant performance challenges for personalized federated learning, and these challenges are mainly reflected in overfitting and low precision in existing methods. To overcome these challenges, a multi-layer multi-fusion strategy framework is proposed in this paper, i.e., the server adopts the network layer parameters of each client upload model as the basic unit of fusion for information-sharing calculation. Then, a new fusion strategy combining personalized and generic is purposefully proposed, and the network layer number fusion threshold of each fusion strategy is designed according to the network layer function. Under this mechanism, the L2-Norm negative exponential similarity metric is employed to calculate the fusion weights of the corresponding feature extraction layer parameters for each client, thus improving the efficiency of heterogeneous data personalized collaboration. Meanwhile, the federated global optimal model approximation fusion strategy is adopted in the network full-connect layer, and this generic fusion strategy alleviates the overfitting introduced by forceful personalized. Finally, the experimental results show that the proposed method is superior to the state-of-the-art methods.