Federated class-incremental learning (FCIL) becomes substantially harder when clients observe different label subsets, progress through tasks at different stages, and provide uneven supervision for the same semantic concepts. Existing FCIL methods often preserve old knowledge through input-space synthesis, but they can be fragile under heterogeneous task streams and difficult to transfer across modalities. To alleviate such issues, we propose PRO, a framework that replaces synthetic input replay with projected rehearsal orchestration. To remove external pretraining, we evaluate all methods under the same warmup. After this, PRO maintains compact class-level projected memories on the server and allows clients perform balanced pseudo multi-task training over current examples and old projected memories. To handle stronger representation drift, we further introduce PRO-MAX, which augments PRO with neighborhood-weighted memory alignment while preserving the same server-light principle that the server only aggregates model updates and memory statistics. Across image, text, and graph benchmarks, PRO and PRO-MAX improve retention and final utility under heterogeneous streams while remaining competitive in homogeneous FCIL. Even when baselines are given expanded replay budgets, they degrade under supervision imbalance and stage misalignment, indicating that replay quantity alone does not resolve replay-quality failures. Additional weak-task diagnostics further show that larger replay mismatch is associated with larger downstream degradation, while our method keeps projected memories better aligned with the evolving representation.

翻译：联邦类增量学习（FCIL）在客户端观测不同标签子集、以不同阶段推进任务、并为相同语义概念提供非均衡监督时变得极具挑战性。现有FCIL方法通常通过输入空间合成保留旧知识，但在异构任务流下易显脆弱且难以跨模态迁移。为缓解这些问题，我们提出PRO框架，用投影排练编排替代合成输入重放。为移除外部预训练，我们以相同预热方式评估所有方法。此后，PRO在服务器端维护紧凑的类级投影记忆，允许客户端对当前样本与旧投影记忆执行均衡的伪多任务训练。针对更强的表示漂移问题，我们进一步引入PRO-MAX，该方法在保留服务器端仅聚合模型更新与记忆统计量的轻量原则基础上，通过邻域加权记忆对齐增强PRO。在图像、文本和图基准测试中，PRO与PRO-MAX在异构流下提升了保留率与最终效用，同时在同构FCIL中保持竞争力。即便为基线方法扩展重放预算，它们在监督不平衡与阶段错位下仍会退化，表明单纯增加重放量无法解决重放质量失效问题。额外弱任务诊断进一步显示，更大的重放不匹配将导致更严重的下游退化，而我们的方法能使投影记忆与演变中的表示保持更优对齐。