Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most existing models unreasonably assume that data categories of FL framework are known and fxed in advance. It renders the global model to signifcantly degrade recognition performance on old categories (i.e., catastrophic forgetting), when local clients receive new categories consecutively under limited memory of storing old categories. Moreover, some new local clients that collect novel categories unseen by other clients may be introduced to the FL training irregularly, which further exacerbates the catastrophic forgetting on old categories. To tackle the above issues, we propose a novel Local-Global Anti-forgetting (LGA) model to address local and global catastrophic forgetting on old categories, which is a pioneering work to explore a global class-incremental model in the FL feld. Specifcally, considering tackling class imbalance of local client to surmount local forgetting, we develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss. They can balance heterogeneous forgetting speeds of hard-to-forget and easy-to-forget old categories, while ensure intrinsic class relations consistency within different incremental tasks. Moreover, a proxy server is designed to tackle global forgetting caused by Non-IID class imbalance between different clients. It collects perturbed prototype images of new categories from local clients via prototype gradient communication under privacy preservation, and augments them via self-supervised prototype augmentation to choose the best old global model and improve local distillation gain. Experiments on representative datasets verify superior performance of our model against other comparison methods.

翻译：联邦学习（FL）是一种通过聚合分散本地客户端模型参数的热门协同训练框架。然而，现有模型大多不合理地假设FL框架的数据类别是预先已知且固定不变的。当本地客户端在存储旧类别数据的有限内存条件下连续接收新类别时，这会导致全局模型对旧类别的识别性能显著下降（即灾难性遗忘）。此外，一些收集其他客户端未见新类别的新本地客户端可能会不定期引入FL训练，进一步加剧对旧类别的灾难性遗忘。为解决上述问题，我们提出了一种新颖的局部-全局抗遗忘（LGA）模型，用于处理旧类别上的局部和全局灾难性遗忘，这是探索FL领域全局类增量模型的先驱性工作。具体而言，针对解决局部客户端的类别不平衡以克服局部遗忘，我们开发了类别平衡梯度自适应补偿损失和类别梯度诱导语义蒸馏损失。它们能够平衡难遗忘与易遗忘旧类别的异质遗忘速度，同时确保不同增量任务间内在类别关系的一致性。此外，我们设计了一个代理服务器来解决由不同客户端间非独立同分布类别不平衡导致的全局遗忘。它在隐私保护条件下通过原型梯度通信收集本地客户端新类别的扰动原型图像，并通过自监督原型增强对其进行扩充，以选择最佳旧全局模型并提升局部蒸馏增益。在代表性数据集上的实验验证了我们模型相较于其他对比方法的优越性能。