One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data distributions. In particular, the interaction of domain shift and label shift across clients induces misaligned feature representations that cannot be corrected through iterative optimization. Existing OSFL methods rely on distillation, server-side generation or ensemble-based aggregation, but assume aligned representations or address domain and label shift separately. We introduce SLOT-Align (Single-round, Learning-free Optimal Transport Alignment), a geometry-aware feature harmonization framework for OSFL. SLOT-Align uses a shared frozen encoder to extract compact feature statistics, constructs a global reference via Bures-Wasserstein barycenters, and aligns local representations using closed-form geodesic optimal transport maps. The method is computationally efficient and can be combined with existing OSFL pipelines relying on frozen encoders without modifying their training procedures. Extensive experiments across multiple benchmarks, pretrained backbones, and OSFL methods show that SLOT-Align consistently improves accuracy and robustness under joint domain and label shift.

翻译：单轮联邦学习（OSFL）旨在解决客户端仅与服务器通信一次的极端通信场景，这会加剧客户端数据异构分布的影响。特别是，跨客户端的域偏移和标签偏移相互作用会导致特征表征错位，而这种错位无法通过迭代优化加以纠正。现有OSFL方法依赖知识蒸馏、服务端生成或基于集成的聚合策略，但这些方法要么假设表征已对齐，要么分别处理域偏移和标签偏移。我们提出SLOT-Align（单轮无训练最优传输对齐），这是一个面向OSFL的几何感知特征协调框架。SLOT-Align利用共享冻结编码器提取紧凑特征统计量，通过Bures-Wasserstein重心构建全局参考，并采用闭式测地线最优传输映射对齐局部表征。该方法计算高效，可与现有依赖冻结编码器的OSFL流水线结合使用，且无需修改其训练流程。在多个基准测试、预训练骨干网络及OSFL方法上的广泛实验表明，SLOT-Align在联合域偏移与标签偏移场景下能持续提升模型准确率与鲁棒性。