Personalized Federated Learning Algorithm Based on Multi-Layer Hypernetwork Aggregation

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Personalized Federated Learning (pFL) algorithms have significant advantages in handling non-Independent and Identically Distributed (non-IID) datasets and enabling client-side model personalization. Hypernetwork-based pFL utilizes the client's own hypernetwork to achieve a personalized client model. However, the effect of sharing client-side hypernetwork parameters and client-side data on the accuracy of client-side personalized models remains unclear. A personalized Federated learning with Multi-layer Hypernetwork (pFedMHN) framework is proposed to optimize client models through the weighted aggregation of local and global hypernetworks. The server learns a global hypernetwork and each client's multi-layer local hypernetworks and then aggregates them. Clients use these aggregated hypernetwork parameters to iteratively update their models, resulting in more accurate personalized models. The experimental results show that on general public datasets, the pFedMHN outperforms the four benchmark algorithms in terms of accuracy, effectively solving the problems of data heterogeneity and model accuracy faced during personalized federated learning on non-IID datasets and achieving a more accurate personalized model for clients by utilizing hypernetwork parameters and client data sharing.