A Model-Heterogeneous Federated Learning Method for Mitigating Domain Shift via Virtual Feature Generation

Federated learning (FL) becomes particularly challenging when model heterogeneity and domain shift occur simultaneously: heterogeneous architectures prevent direct parameter aggregation, while domain-dependent feature distributions hinder effective knowledge transfer across clients. Prototype-based approaches alleviate model heterogeneity by exchanging class-level feature summaries, yet a single prototype captures only the distribution center and overlooks domain-dependent variations in feature spread. To address these challenges, we propose FedVFG, a model-heterogeneous Federated Learning method for mitigating domain shift through Virtual Feature Generation. Each client summarizes its local feature distributions using class prototypes and diagonal variances, which are scale-calibrated and aggregated by domain on the server. During local training, clients interpolate domain-specific class prototypes and standard deviations to construct virtual domain statistics, which guide the transformation of local features into class-conditioned virtual features. The original and virtual features are then jointly used to optimize the local models, enabling them to learn from cross-domain feature variations across heterogeneous client models. Experiments on Digit-5 and PACS under domain shift in the model heterogeneous setting show that FedVFG achieves state-of-the-art performance, outperforming the strongest baselines by 6.54% and 1.66% in average accuracy, respectively.