<ul><li>FLORA is a novel machine learning approach that leverages federated learning and parameter-efficient adapters to train visual-language models (VLMs) in distributed environments.</li><li>FLORA utilizes Low-Rank Adaptation (LoRA) in combination with Federated Learning to fine-tune VLMs like the CLIP model, preserving data privacy and minimizing communication overhead.</li><li>Experimental evaluations show FLORA's superior accuracy, efficiency, and adaptability compared to traditional federated learning methods in both IID and non-IID settings.</li><li>FLORA's efficient model adaptation addresses real-world data challenges in distributed learning environments, making it a promising solution.</li></ul>

This AI Paper Proposes FLORA: A Novel Machine Learning Approach that Leverages Federated Learning and Parameter-Efficient Adapters to Train Visual-Language Models VLMs

This AI Paper Proposes FLORA: A Novel Machine Learning Approach that Leverages Federated Learning and Parameter-Efficient Adapters to Train Visual-Language Models VLMs

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