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

ArXi:2509.06984v3 Announce Type: replace Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, participating institutions often possess heterogeneous computational resources, resulting in imbalanced LoRA ranks, which pose a major challenge for effective collaboration.