One-Shot Split Federated Learning with Prototype-Guided Synthetic Supervision

Split Federated Learning (SFL) enables collaborative training across resource-constrained clients by partitioning models between clients and the server. However, under strict communication constraints, reducing client-server interaction to a single round severely limits available supervision, making effective training challenging under heterogeneous and imbalanced data distributions. In this work, we investigate one-shot SFL and identify the lack of reliable global supervision from limited intermediate representations as the key challenge. Existing one-shot federated learning methods typically assume access to complete local models or full model aggregation, making them difficult to apply to split federated learning where clients only optimize partial models. To address this issue, we propose Heterogeneous One-shot Split Federated Learning (HOSFL), a framework that constructs class-aware supervision from one-shot uploaded representations. HOSFL aggregates cross-client class prototypes and employs prototype-guided feature alignment and classifier-guided class consistency constraints to generate class-balanced synthetic samples, enabling effective model optimization within a single communication round. Extensive experiments on multiple benchmarks under diverse non-IID settings demonstrate that HOSFL achieves consistent performance improvements while significantly reducing communication overhead, with an average accuracy gain of 4.72% over strong SFL baselines. The anonymous code is available at https://anonymous.4open.science/r/HOSFL-10B3.