Federated Learning aims to learn a global model on the server side that generalizes to all clients in a privacy-preserving manner, by leveraging the local models from different clients. Existing solutions focus on either regularizing the objective functions among clients or improving the aggregation mechanism for the improved model generalization capability. However, their performance is typically limited by the dataset biases, such as the heterogeneous data distributions and the missing classes. To address this issue, this paper presents a cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side. Specifically, FedCSPC first employs the Data Prototypical Modeling (DPM) module to learn data patterns via clustering to aid calibration. Subsequently, the cross-silo prototypical calibration (CSPC) module develops an augmented contrastive learning method to improve the robustness of the calibration, which can effectively project cross-source features into a consistent space while maintaining clear decision boundaries. Moreover, the CSPC module's ease of implementation and plug-and-play characteristics make it even more remarkable. Experiments were conducted on four datasets in terms of performance comparison, ablation study, in-depth analysis and case study, and the results verified that FedCSPC is capable of learning the consistent features across different data sources of the same class under the guidance of calibrated model, which leads to better performance than the state-of-the-art methods. The source codes have been released at https://github.com/qizhuang-qz/FedCSPC.

翻译：联邦学习旨在通过利用来自不同客户端的本地模型，以保护隐私的方式在服务端学习一个能泛化到所有客户端的全局模型。现有方法主要集中在正则化不同客户端间的目标函数或改进聚合机制以提升模型泛化能力。然而，其性能通常受限于数据集偏差，例如异构数据分布和类别缺失。为解决此问题，本文提出了一种跨域原型校准方法（FedCSPC），该方法从客户端获取额外的原型信息，以在服务端学习统一特征空间。具体而言，FedCSPC首先使用数据原型建模（DPM）模块，通过聚类学习数据模式以辅助校准。随后，跨域原型校准（CSPC）模块开发了一种增强对比学习方法，提升校准的鲁棒性，能有效将跨源特征投影到一致的空间，同时保持清晰的决策边界。此外，CSPC模块易于实现且即插即用的特性使其尤为突出。在四个数据集上进行了性能对比、消融研究、深入分析与案例研究，实验结果证实，FedCSPC能够在校准模型引导下，学习同类不同数据源间的一致性特征，从而取得优于最新方法的性能。源代码已发布于https://github.com/qizhuang-qz/FedCSPC。