Federated learning for computer vision is an emerging research area that combines decentralized, privacy-preserving model training with visual data analytics, enabling multiple clients or edge devices to collaboratively train deep learning models without sharing raw images or videos. This approach is particularly relevant for applications where data privacy is critical, such as medical imaging, surveillance, autonomous driving, and industrial inspection. Research focuses on federated optimization techniques, handling heterogeneous and non-i.i.d. visual data, communication-efficient training strategies, and integration with state-of-the-art architectures including CNNs, vision transformers (ViTs), and graph neural networks (GNNs). Recent studies also explore personalization strategies, adversarial robustness, privacy preservation using differential privacy or secure aggregation, and cross-domain knowledge transfer to enhance performance across diverse visual datasets. Applications demonstrate improvements in image classification, object detection, segmentation, and action recognition while maintaining data privacy, establishing federated learning as a promising paradigm for distributed computer vision systems.