What to Represent and What to Remember: Complementary Replay for Federated Class-Incremental Learning

Exemplar replay-based federated class-incremental learning aims to preserve previously acquired knowledge by replaying selected samples while learning new classes. Existing methods independently score samples in the full feature space and replay those with the highest importance. However, they overlook complementarity within the limited replay set, resulting in redundant selections and insufficient coverage of the historical representation space accumulated across clients and tasks. To address these limitations, this study proposes a federated REpresentation Coverage-Aware repLay Learning framework, termed RECALL, which constructs a compact and complementary replay memory through global critical-feature identification and residual-based exemplar selection. Specifically, it consists of three modules. The global critical feature selection module selects feature coordinates for reconstructing class-balanced current and previous model representations, reducing feature redundancy. The complementary exemplar selection module combines local residual preselection with class-wise global coordination to reduce cross-client redundancy and improve replay coverage. The coverage-weighted feature distillation module constrains representation drift along the selected critical directions, preserving historical knowledge during incremental learning. We also bound full-class reconstruction error for fixed representations in the selected coordinates, accounting for local screening loss and client quotas. Experiments across three benchmark datasets show that RECALL consistently outperforms strong baselines by 1%–8%, validating the effectiveness of complementary and coverage-aware replay.