arXiv 2603.030072026-03-03cs.LGcs.DC

打破原型偏见循环：面向高度不平衡客户端的置信度感知联邦对比学习

Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clients

Key Laboratory of Water Big Data Technology of Ministry of Water Resources, College of Computer Science and Software Engineering, Hohai University, Nanjing, China（水利部水大数据技术重点实验室，计算机科学与软件工程学院，河海大学，南京，中国）；Department of Computer Science, City University of Hong Kong, Hong Kong, China（计算机科学系，香港城市大学，香港，中国）；State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China（新型软件技术国家重点实验室，南京大学，南京，中国）；School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China（人工智能与信息技术学院，南京中医药大学，南京，中国）

Tian-Shuang Wu, Shen-Huan Lyu, Ning Chen, Yi-Xiao He, Bing Tang, Baoliu Ye, Qingfu Zhang

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AI总结：

本研究提出CAFedCL框架，通过置信度感知聚合和生成增强等方法，解决联邦学习中因数据不平衡和异质性导致的原型偏见问题，提升模型准确性和客户端公平性。

摘要：

局部类别不平衡和客户端间的数据异质性常常使基于原型的联邦对比学习陷入原型偏见循环：由不平衡数据诱导的偏置局部原型被聚合到偏置全局原型中，这些原型被反复用作对比锚点，在通信轮次中积累误差。为打破此循环，我们提出了一种新的框架，即置信度感知联邦对比学习（CAFedCL），该框架改进了原型聚合机制并加强了由原型引导的对比对齐。CAFedCL采用一种置信度感知聚合机制，利用预测不确定性来降低高方差局部原型的权重。此外，为少数类生成增强和几何一致性正则化被整合进来，以稳定类间结构。从理论角度，我们提供了一种基于期望的分析，证明我们的聚合减少了估计方差，从而界定了全局原型漂移并确保收敛。在不同类别不平衡程度和数据异质性下的广泛实验表明，CAFedCL在准确率和客户端公平性方面均优于代表性联邦基线。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。