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 of addressing various degrees of domain shift. Essentially, the hypernetwork supports a non-linear fusion of client models enabling a comprehensive understanding of the underlying data distribution. We encompass an extensive discussion and provide novel insights into the tradeoff between personalization and generalization in FL. The proposed algorithm outperforms strong benchmarks on three widely-used data sets for DG in an exceeding number of cases.
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