Hypernetwork-Driven Model Fusion for Federated Domain Generalization

Understand

Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data, degrading performance.

• Traditional domain generalization aims to learn domain-invariant features, but the federated nature of model averaging often limits this due to its linear aggregation of local learning.
• To address this, we propose a robust framework, coined as hypernetwork-based Federated Fusion (hFedF), using hypernetworks for non-linear aggregation, facilitating generalization to unseen domains.
• Our method employs client-specific embeddings and gradient alignment techniques to manage domain generalization effectively.