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

Excerpt

arXiv:2607.12112v2 Announce Type: replace-cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations. Addressing this challenge is especially critical for