Non-linear Fusion in Federated Learning: A Hypernetwork Approach to Federated Domain Generalization

Federated Learning (FL) has emerged as a promising paradigm in which multiple clients collaboratively train a shared global model while preserving data privacy. To create a robust and practicable FL framework, it is crucial to extend its ability to generalize well to unseen domains - a problem referred to as federated Domain Generalization (FDG), being still under-explored. We propose an innovative federated algorithm, termed hFedF for hypernetwork-based Federated Fusion, designed to bridge the performance gap between generalization and personalization, capable …

cs.lg data data privacy domain domains federated learning framework fusion global linear multiple non-linear paradigm privacy robust train