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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FLORA is a novel machine learning approach that leverages federated learning and parameter-efficient adapters to train vision-language models (VLMs). It addresses the challenges of privacy and scalability by allowing models to be trained across a distributed network of devices while keeping data locally. FLORA uses Low-Rank Adaptation to selectively update a small subset of the model's parameters, accelerating training time and reducing memory usage.

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