Impact of Procrustes Alignment on Cross-Client Heterogeneity Robustness in Federated Multimodal Models

Abstract

Abstract: The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolution

Research Question

What is the impact of Procrustes alignment on cross-client heterogeneity robustness in federated multimodal models evaluated on standard vision-language reasoning datasets?

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