OFLBe: Do Our Best to Make One-Shot Federated Learning Better

One-shot federated learning (OFL) enables collaborative training across multiple clients through a single round of client-server interaction, substantially reducing communication costs compared to traditional multi-round FL. However, under non-IID data settings, clients perform local training independently without multi-round interactions with the server to align the representation space across clients, leading to severe feature drift. To address this issue, we propose OFLBe, an OFL framework based on consistent anchor semantic space optimization. OFLBe leverages a pre-trained CLIP text encoder to construct shared class-level semantic anchors, providing a unified semantic representation space reference for all clients. During local training, OFLBe introduces an anchor-guided feature alignment loss and a learnable prototype loss to guide cross-client learning toward a consistent representation space with intra-class compactness and inter-class discriminability. Moreover, on the server side, instead of directly averaging the clients’ model parameters, OFLBe aggregates the uploaded local prototypes and the features output by the local models during inference based on the similarity between the uploaded local prototypes and the class-level semantic anchors, thereby maximizing the utilization of knowledge from a single client-server interaction. Experiments on three datasets under various data heterogeneity settings demonstrate that OFLBe achieves the best performance compared with 12 OFL baselines.