  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. Evaluated in both zero-shot and few-shot settings,
hFedF demonstrates superior performance in handling domain shifts.
Comprehensive comparisons on PACS, Office-Home, and VLCS datasets show that
hFedF consistently achieves the highest in-domain and out-of-domain accuracy
with reliable predictions. Our study contributes significantly to the
under-explored field of Federated Domain Generalization (FDG), setting a new
benchmark for performance in this area.
