Limited training data and severe class imbalance impose significant challenges to developing clinically robust deep learning models. Federated learning (FL) addresses the former by enabling different medical clients to collaboratively train a deep model without sharing data. However, the class imbalance problem persists due to inter-client class distribution variations. To overcome this, we propose federated classifier anchoring (FCA) by adding a personalized classifier at each client to guide and debias the federated model through consistency learning. Additionally, FCA debiases the federated classifier and each client's personalized classifier based on their respective class distributions, thus mitigating divergence. With FCA, the federated feature extractor effectively learns discriminative features suitably globally for federation as well as locally for all participants. In clinical practice, the federated model is expected to be both generalized, performing well across clients, and specialized, benefiting each individual client from collaboration. According to this, we propose a novel evaluation metric to assess models' generalization and specialization performance globally on an aggregated public test set and locally at each client. Through comprehensive comparison and evaluation, FCA outperforms the state-of-the-art methods with large margins for federated long-tailed skin lesion classification and intracranial hemorrhage classification, making it a more feasible solution in clinical settings.

翻译：有限的训练数据和严重的类别不平衡对开发临床稳健的深度学习模型构成了重大挑战。联邦学习（FL）通过使不同医疗客户端在不共享数据的情况下协作训练深度模型，解决了前一个问题。然而，由于客户端间类别分布差异，类别不平衡问题仍然存在。为克服这一难题，我们提出了联邦分类器锚定（FCA），通过在每个客户端添加个性化分类器，借助一致性学习引导并去偏联邦模型。此外，FCA基于各自的类别分布对联邦分类器和每个客户端的个性化分类器进行去偏，从而缓解了发散问题。借助FCA，联邦特征提取器能够有效地学习判别性特征，既全局适用于联邦协作，又局部适用于所有参与者。在临床实践中，联邦模型应同时具备泛化性（在所有客户端中表现良好）和专化性（使每个独立客户端从协作中受益）。据此，我们提出了一种新的评估指标，用于在全局聚合公开测试集和局部每个客户端上评估模型的泛化性与专化性性能。通过全面比较与评估，FCA在联邦长尾皮肤病变分类和颅内出血分类中大幅超越了现有最先进方法，成为临床环境中更可行的解决方案。