Local class imbalance and data heterogeneity across clients often trap prototype-based federated contrastive learning in a prototype bias loop: biased local prototypes induced by imbalanced data are aggregated into biased global prototypes, which are repeatedly reused as contrastive anchors, accumulating errors across communication rounds. To break this loop, we propose Confidence-Aware Federated Contrastive Learning (CAFedCL), a novel framework that improves the prototype aggregation mechanism and strengthens the contrastive alignment guided by prototypes. CAFedCL employs a confidence-aware aggregation mechanism that leverages predictive uncertainty to downweight high-variance local prototypes. In addition, generative augmentation for minority classes and geometric consistency regularization are integrated to stabilize the structure between classes. From a theoretical perspective, we provide an expectation-based analysis showing that our aggregation reduces estimation variance, thereby bounding global prototype drift and ensuring convergence. Extensive experiments under varying levels of class imbalance and data heterogeneity demonstrate that CAFedCL consistently outperforms representative federated baselines in both accuracy and client fairness.

翻译：客户端间的局部类别不平衡与数据异质性常使基于原型的联邦对比学习陷入原型偏差循环：由不平衡数据引发的有偏局部原型被聚合为有偏全局原型，这些原型又作为对比锚点被重复使用，导致误差在通信轮次间持续累积。为打破此循环，本文提出置信感知联邦对比学习（CAFedCL），该新颖框架改进了原型聚合机制，并强化了原型引导的对比对齐。CAFedCL采用置信感知聚合机制，利用预测不确定性对高方差局部原型进行降权处理。此外，框架整合了针对少数类别的生成式增强与几何一致性正则化，以稳定类别间结构。从理论角度，我们提供了基于期望的分析，证明所提聚合方法能降低估计方差，从而限制全局原型漂移并确保收敛性。在不同程度的类别不平衡与数据异质性场景下的广泛实验表明，CAFedCL在分类精度与客户端公平性方面均持续优于代表性联邦学习基线方法。