Federated Learning for Image Captioning: A Comprehensive Review of Privacy-Preserving Collaborative Model Training in Distributed Environments

Abstract

This study presents a comprehensive review of the use of federated learning in the context of image captioning in distributed environments. It focuses on key aspects such as privacy preservation, data locality, and collaborative model training. The evolution of federated learning and its unique characteristics are explored, along with an examination of available open-source frameworks specific to image captioning. The study categorizes different approaches to federated learning for image captioning and showcases recent applications in diverse domains, including medical imaging, edge computing, autonomous vehicles, social media, and cross-domain image analysis. Additionally, optimization techniques, security analysis, and research challenges are discussed, encompassing data heterogeneity, privacy preservation, communication efficiency, limited labeling, scalability, and robustness against adversarial attacks. This comprehensive review contributes to a deeper understanding of federated learning for image captioning and highlights areas for further research and advancement in the field.