FedSSP: Shared-Branch Stitching into Private Models for Heterogeneous Federated Learning

Heterogeneous Federated Learning (HtFL) has attracted increasing attention for its ability to support collaboration under both model and data heterogeneity. However, existing methods require visibility into private architectures, rely on public datasets, or introduce additional asymmetric knowledge transfer. To overcome these limitations, We propose FedSSP, which represents transferable global knowledge as a shared branch stitched into individual private models. Through a lightweight stitching layer, FedSSP jointly optimizes shared knowledge injection and local personalization in single-stage dual-path training, without a standalone proxy or separate distillation. FedSSP selects client-specific stitch positions by complementing intra-model KL-based block capacity with cross-entropy improvement to account for local task utility. For aggregation, shared-feature shifts and relative private-model updates serve as complementary contribution proxies, capturing representation changes and local adaptation. Extensive experiments on various datasets with eleven model architectures show that FedSSP consistently outperforms existing HtFL approaches, achieving an average accuracy improvement of 7.24%, without sharing any private model parameters or relying on public datasets.