FedRS: Federated Learning Under Reliable Supervision for Multi-Organ Segmentation With Inconsistent Labels

Existing multi-organ segmentation methods usually rely on large and fully labeled datasets for training. However, medical image datasets are typically decentralized by privacy constraints and partially labeled due to the high costs of full annotation in clinical practice, resulting in label inconsistency across medical centers. Federated learning offers privacy-preserving decentralized training, but the label inconsistency leads to significant divergence in local model parameters across medical centers, thereby hindering the achievement of the global optimum. To resolve this issue, an effective and communication-efficient Federated Learning under Reliable Supervision (FedRS) is proposed, which ensures: i) the local models are trained with reliable supervisory information through the proposed Less-Forgetting and Less-Constraint loss functions, thereby reducing the divergence in local model parameters; and ii) the global model is aggregated based on the consistency of predictions between each local model (after local training) and the global model (received before training), thereby enhancing the reliability of the global model. Extensive experimental results on nine publicly available 3D abdominal CT image datasets show that our FedRS outperforms localized, centralized, and state-of-the-art federated learning methods on both in-federation and out-of-federation datasets, demonstrating its effectiveness and strong generalization capability. In particular, our FedRS only utilizes a model with only 4.1M parameters as its backbone, thereby significantly reducing its communication cost. The source code is publicly available at https://github.com/luohy812/FedRS.