FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints

In three linesFediLoRA introduces a federated LoRA fine-tuning framework for vision-language models (VLLMs) addressing imbalanced LoRA ranks from heterogeneous resources and missing modalities from user errors or device failures. The method combines simple averaging with structured editing, validated on general-domain and medical-domain benchmarks.

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