Bridging Model Heterogeneity in Federated Learning with Group Prototypical Alignment
The heterogeneous nature has been regarded as a predominant challenge during the deployment of federated learning (FL) systems, wherein model heterogeneity—where clients train models of fundamentally different architectures—remains underexplored. Existing methods tolerate it poorly: they enforce interdependent model families, extract sub-models of one shared model, or rely on auxiliary public datasets and proxy models, which constrain model selection or demand storage that resource-limited clients cannot afford. We posit that an effective bridge across heterogeneous models should be both model-agnostic—decoupling knowledge transfer from incompatible parameter spaces—and storage-free, staying practical for clients with sharply different resources. On this basis we propose FedGPA, a hierarchical framework in which clients with comparable resources and identical architectures form a group and a server mediates knowledge transfer across groups of diverse models; at its core, the lightweight, model-agnostic Aligned Co-decision (Alco) Unit aligns class-level prototypical information across groups to bridge heterogeneous architectures, and a prototypical fusion step interpolates prototypes to regularize local training and refine cross-group knowledge. Experiments on CIFAR-10/100, EMNIST, and Tiny-ImageNet across heterogeneous architectures show that FedGPA consistently outperforms strong baselines from four method families, with the largest gains on resource-poor clients, at a cost of only ~100 KB of additional cross-group communication per round. A limitation of this study is that we provide no formal differential-privacy guarantee for the exchanged prototypes and do not address adversarial or data-quality attacks, leaving both to future work.