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Proposes FedCMM, a framework for federated continual learning of multimodal LLMs that uses modality-aware elastic weight consolidation, local generative replay, and task-similarity-aware gradient aggregation to mitigate catastrophic forgetting.
The paper addresses catastrophic forgetting in sequentially trained early-exiting neural networks and proposes two methods based on Elastic Weight Consolidation and Learning without Forgetting to preserve earlier exit performance while adding new ones.