Co-Evolving Rehearsal States for Federated Class-Incremental Learning under Asynchronous Class Exposure

Federated class incremental learning (FCIL) must retain earlier classes while clients continue learning from different class histories. At the same round, a class may be current for one client, historical for another, and unseen by a third. We call this disagreement asynchronous class exposure (ACE). Existing methods preserve history through replay, prototypes, feature memories, or parameter constraints, but they often treat stored evidence as fixed while the model representation continues to change. As a result, historical evidence can bypass trainable modules, use incompatible encoder coordinates, or become stale over time. We address this gap with Projected Rehearsal Orchestration (PRO), which rehearses current and remembered evidence before the adapter and rebuilds available class statistics under the aggregated global encoder. When fresh examples are unavailable, PRO-MAX estimates class local feature movement from current calibration data and combines client estimates using support weights to maintain the remaining memories. Across six image, text, and graph benchmarks, PRO and PRO-MAX improve retention under heterogeneous exposure, with PRO-MAX achieving the highest reported mean final accuracy among the evaluated methods on all six streams. We further show that the gains are not explained by feature sampling alone. Instead, effective rehearsal depends on keeping historical evidence trainable, compatible with the current representation, and updated as learning evolves.