Iosifidis, AntoniosAntoniosIosifidisKaragiannis, VasileiosVasileiosKaragiannisSchulte, StefanStefanSchulte2026-08-172026-08-172026-0510th IEEE International Conference on Fog and Edge Computing, ICFEC 2026https://hdl.handle.net/11420/64405In Hierarchical Federated Learning (HFL), bandwidth budgets and delay constraints can prevent the timely synchronization of the semantic atlases (summaries) required for clients to learn unseen classes. Conventional synchronization policies typically optimize for geometric freshness. However, staleness becomes most harmful when it changes discrete curriculum gating decisions. We find that reducing feature space drift alone is an unreliable proxy for knowledge void recovery under constraints, because learning depends on decision consistency. Our Decision-Critical Atlas Synchronization (DCAS) policy prioritizes updates that are likely to alter these decisions and accelerates recovery from knowledge voids compared to a geometry-centric baseline.enPrototypesCloudsLearning (artificial intelligence)PrintingFederated learningModelingLabelingSynchronizationPlaningEquationsComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceConference Paper10.1109/ICFEC69006.2026.00018