The recent trend of migrating computation from the centralized cloud to distributed edge devices is reshaping the landscape of today’s Internet. Distributed machine learning, specifically federated learning (FL), has been envisioned as a key technology for enabling next generation AI at-scale. Moreover, with privacy being a critical concern in data aggregation, FL emerges as a promising solution to such privacy-utility challenges. It pushes the computation towards the consumer’s edge devices, where the data is generated. By exchanging statistical information rather than the original data, the participants perform collaborative learning in a distributed fashion.
Although FL has become an important privacy-preserving paradigm in various machine learning tasks, the potential of FL in computer vision (CV) applications, such as face recognition, person re-identification, and action recognition, is far from being fully exploited. Moreover, FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection, image segmentation, and video understanding, compared to the traditional centralized training paradigm.
This workshop aims at bringing together researchers and practitioners with common interest in FL for computer vision. This workshop is an attempt at studying the different synergistic relations in this interdisciplinary area. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to discuss the future research challenges and opportunities.