MLFCIL decomposes forgetting into local, cross-task, and federation sources and mitigates them via class-reweighted loss, knowledge distillation with replay and drift compensation, class-aware aggregation, and dual-granularity coordination.
Joint Computation Offload- ing and Resource Management for Cooperative Satellite–Aerial–Marine Internet of Things Networks
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MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
MLFCIL decomposes forgetting into local, cross-task, and federation sources and mitigates them via class-reweighted loss, knowledge distillation with replay and drift compensation, class-aware aggregation, and dual-granularity coordination.