FedCL: Federated contrastive learning for multi-center medical image classification

•We propose a federated contrastive learning method, which uses the feature representation learned by the global model and the local model for contrastive learning, so that the final model can gradually approach the global model in the training process to obtain better model generalization.•In view of the current problem of data heterogeneity in real medical scenes, our method effectively reduces its impact on experimental results, that is, it can show high performance and generalization in the scenario of data heterogeneity.•We conducted experiments on two medical image classification datasets, i.e., skin lesion classification dataset for melanoma and chest X-ray dataset for detecting COVID-19. Extensive experiments demonstrate the efficacy of our approach.