  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.
