Organizers: Chen Chen, Guangyu Sun, Nathalie Baracaldo, Victor Zhu, Nicholas Lane, Yang Liu, Mahdi Morafah, Aritra Dutta, Zhishuai Guo

Date: TBD (June 2026)
Time: TBD
Location: TBD

Summary: This workshop aims at bringing together researchers and practitioners with common interest in federated learning for computer vision. This workshop is an attempt at studying the different synergistic relations in this interdisciplinary area. This half-day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to discuss the future research challenges and opportunities.

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.

Paper (& supplementary material) Submission Deadline: March 20, 2026 (11:59 PM, PST)
Notification: April 6, 2026 (11:59 PM, PST)
Camera-Ready: April 11, 2026 (11:59 PM, PST)

Accepted papers will be published in conjunction with CVPR 2026 proceedings.
Paper submissions will adhere to the CVPR 2026 paper submission style, format, and length restrictions.


The CVPR 2026 author kit is available here.
Paper submission website is here.
For any questions, please contact Dr. Chen Chen.

Acknowledgement: The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

• Axon will sponsor a best paper award for FedVision 2026!
• FedVision has been accepted by CVPR 2026!

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The website is created by Guangyu Sun.

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.