Federated Learning in Computer Vision

Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings. Computer vision tasks deal with huge datasets often with critical privacy issues, therefore many federated learning approaches have been presented to exploit its distributed and privacy-preserving nature. Firstly, this paper introduces the different FL settings used in computer vision and the main challenges that need to be tackled. Then, it provides a comprehensive overview of the different strategies used for FL in vision applications and presents several different approaches for image classification, object detection, semantic segmentation and for focused settings in face recognition and medical imaging. For the various approaches the considered FL setting, the employed data and methodologies and the achieved results are thoroughly discussed.