ACFL: Communication-Efficient adversarial contrastive federated learning for medical image segmentation

Federated learning is a popular machine learning paradigm that achieves decentralized model training on distributed devices, ensuring data decentralization, privacy protection, and enhanced overall learning effectiveness. However, the non-independence and identically distributed (i.e., non-IID) nature of medical data across different institutes has remained a significant challenge in federated learning. Current research has mainly focused on addressing label distribution skew and classification scenarios, overlooking the feature distribution skew settings and more challenging semantic segmentation scenarios. In this paper, we present communication-efficient Adversarial Contrastive Federated Learning (ACFL) for the prevalent feature distribution skew scenarios in medical semantic segmentation. The core idea of the approach is to enhance model generalization by learning each client's domain-invariant features through adversarial training. Specifically, we introduce a global discriminator that, through contrastive learning in the server, trains to differentiate feature representations from various clients. Meanwhile, the clients learn common domain-invariant features through prototype contrastive learning and global discriminator training. Furthermore, by utilizing Gaussian mixture models for virtual feature sampling on the server, compared to transmitting raw features, the ACFL method possesses the additional advantages of efficient communication and privacy protection. Extensive experiments on two medical semantic segmentation datasets and extension on three classification datasets validated the superiority of the proposed method.