从选择到调度：联邦几何感知修正使示例回放在持续动态异质性下表现更佳
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
AI总结：
本文提出FEAT方法，通过几何结构对齐模块和能量基几何修正模块，解决联邦持续学习中示例回放在持续动态异质性下的性能问题。
摘要：
示例回放已成为缓解联邦持续学习中灾难性遗忘的有效策略，通过保留过去任务的代表性样本。现有研究侧重于设计样本重要性估计机制以识别信息丰富的样本。然而，它们通常忽略了如何有效利用所选示例的策略，这限制了其在客户和任务间持续动态异质性下的性能。为了解决这个问题，本文提出了一种联邦几何感知修正方法，称为FEAT，该方法缓解了由不平衡引起的表示崩溃，将稀有类特征拉向频繁类。具体来说，它由两个关键模块组成：1）几何结构对齐模块通过对齐特征表示之间的成对角相似性与其对应的等角紧框架原型进行结构知识蒸馏，这些原型在客户端之间固定且共享，用作类辨别参考结构。这鼓励了任务间的几何一致性，并有助于缓解表示漂移；2）基于能量的几何修正模块从特征嵌入中移除任务无关的方向成分，从而减少对多数类的预测偏差。这提高了对少数类的敏感性，并增强了模型在类不平衡分布下的鲁棒性。
英文摘要：
Exemplar replay has become an effective strategy for mitigating catastrophic forgetting in federated continual learning (FCL) by retaining representative samples from past tasks. Existing studies focus on designing sample-importance estimation mechanisms to identify information-rich samples. However, they typically overlook strategies for effectively utilizing the selected exemplars, which limits their performance under continual dynamic heterogeneity across clients and tasks. To address this issue, this paper proposes a Federated gEometry-Aware correcTion method, termed FEAT, which alleviates imbalance-induced representation collapse that drags rare-class features toward frequent classes across clients. Specifically, it consists of two key modules: 1) the Geometric Structure Alignment module performs structural knowledge distillation by aligning the pairwise angular similarities between feature representations and their corresponding Equiangular Tight Frame prototypes, which are fixed and shared across clients to serve as a class-discriminative reference structure. This encourages geometric consistency across tasks and helps mitigate representation drift; 2) the Energy-based Geometric Correction module removes task-irrelevant directional components from feature embeddings, which reduces prediction bias toward majority classes. This improves sensitivity to minority classes and enhances the model's robustness under class-imbalanced distributions.
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。
