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FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning
Duy Phuong Nguyen, J. Pablo Munoz, Ali Jannesari
https://arxiv.org/abs/2404.15182

FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning
In the rapidly evolving field of artificial intelligence, multimodal models, e.g., integrating vision and language into visual-language models (VLMs), have become pivotal for many applications, ranging from image captioning to multimodal search engines. Among these models, the Contrastive Language-Image Pre-training (CLIP) model has demonstrated remarkable performance in understanding and generating nuanced relationships between text and images. However, the conventional training of such models…

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Federated Complex Qeury Answering
Qi Hu, Weifeng Jiang, Haoran Li, Zihao Wang, Jiaxin Bai, Qianren Mao, Yangqiu Song, Lixin Fan, Jianxin Li
https://arxiv.org/abs/2402.14609

Federated Complex Qeury Answering
Complex logical query answering is a challenging task in knowledge graphs (KGs) that has been widely studied. The ability to perform complex logical reasoning is essential and supports various graph reasoning-based downstream tasks, such as search engines. Recent approaches are proposed to represent KG entities and logical queries into embedding vectors and find answers to logical queries from the KGs. However, existing proposed methods mainly focus on querying a single KG and cannot be applied…