H-SFP: Hierarchical Federated Learning with Decoupled Split-Model Prototyping

Federated learning (FL) enables collaborative model trainingwithout sharing raw data, but practical deployments must balance sta-tistical heterogeneity, resource-constrained clients, and communicationefficiency. While conventional FL requires clients to train full modelslocally, split federated learning (SFL) reduces client-side computationby partitioning models across tiers, at the cost of transmitting high-dimensional activations and gradients during training. We propose Hier-archical Split-Federated Prototyping (H-SFP), a communication-efficientframework for hierarchical cloud–edge–client environments. Instead ofexchanging sample-level activations or gradients, H-SFP communicatescompact class-wise feature statistics and uses them to synthesize repre-sentative feature distributions at upper tiers. This statistical prototyp-ing mechanism enables independent training of model segments whilepreserving the computational advantages of split-model execution andsubstantially reducing synchronization and communication overhead. Akey finding of this work is that lightweight first- and second-order fea-ture statistics are sufficient to support hierarchical split-model train-ing, providing an effective communication interface without requiringsample-level feature exchange. We analyze the communication proper-ties and stability of the proposed statistical interface under hierarchi-cal aggregation. Extensive experiments on CIFAR-10/100, HAM10000,ImageNet-1K, and ISIC-2018 demonstrate that H-SFP remains robustunder heterogeneous data distributions while reducing communicationoverhead by up to two orders of magnitude compared with federatedand split-learning baselines.