FedPAM: Federated Personalized Augmentation Model for Text-to-Image Retrieval
Yueying Feng, Fan Ma, Lin Wang, Chang Yao, Jingyuan Chen, Yi Ping Yang · 2024
CLIP-based models have made significant advancements in text-to-image retrieval tasks. However, these retrieval models are typically trained on public datasets with optimizing all parameters, which limits their ability to generalize and adapt quickly to personalized private datasets. In this paper, we introduce a lightweight personalized federated learning solution, namely Federated Personalized Augmentation Model (FedPAM), to achieve personalized text-to-image retrieval from multiple private database. Specifically, for the query text, we fetch the top-k most similar text-image pairs from the private database. We then use an attention-based module to generate personalized representations for different clients. The updated representation includes client-specific information for text-to-image matching, resolving issues of data heterogeneity. Additionally, we ensure efficient and secure communication by fine-tuning a small portion of network parameters. Our experiments demonstrate the effectiveness of the proposed framework, exhibiting a significant performance improvement over recently proposed methods: +5.36 on IAPR TC-12, +2.86 on CC3M, and +1.72 on Flickr30k.