No One Left Behind: Real-World Federated Class-Incremental Learning
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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.
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