Chenxuanyi Zou

Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

Federated Continual Multimodal Learning (FedCMM), a framework that embeds continual-learning safeguards into the federated optimization loop at three complementary levels, is proposed, confirming that holistic, modality-aware optimization enables robust evolutive adaptation across heterogeneous networked AI deployments.