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Federated Learning (FL) enables collaborative model training amongparticipants while guaranteeing the privacy of raw data. Mainstream FLmethodologies overlook the dynamic nature of real-world data, particularly itstendency to grow in volume and diversify in classes over time. This oversightresults in FL methods suffering from catastrophic forgetting, where the trainedmodels inadvertently discard previously learned information upon assimilatingnew data. In response to this challenge, we propose a novel FederatedClass-Incremental Learning (FCIL) method, named \underline{Fed}erated\underline{C}lass-Incremental \underline{L}earning with New-Class\underline{A}ugmented \underline{S}elf-Di\underline{S}tillation (FedCLASS). Thecore of FedCLASS is to enrich the class scores of historical models with newclass scores predicted by current models and utilize the combined knowledge forself-distillation, enabling a more sufficient and precise knowledge transferfrom historical models to current models. Theoretical analyses demonstrate thatFedCLASS stands on reliable foundations, considering scores of old classespredicted by historical models as conditional probabilities in the absence ofnew classes, and the scores of new classes predicted by current models as theconditional probabilities of class scores derived from historical models.Empirical experiments demonstrate the superiority of FedCLASS over fourbaseline algorithms in reducing average forgetting rate and boosting globalaccuracy.

Federated Class-Incremental Learning with New-Class Augmented Self-Distillation