Workshop on Federated Learning for Computer Vision Call for Papers

The Third Workshop on Federated Learning for Computer Vision (FedVision)  

in Conjunction with CVPR 2024 


Federated Learning (FL) has become an important privacy-preserving
paradigm in various machine learning tasks. However, the potential of
FL in computer vision 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 and image
segmentation, compared to the traditional centralized training
paradigm. This workshop aims at bringing together researchers and
practitioners with common interests in FL for computer vision and
studying the different synergistic relations in this interdisciplinary
area. The day-long event will facilitate interaction among students,
scholars, and industry professionals from around the world to discuss
future research challenges and opportunities.


Keynote speakers 

    Dr. Lingjuan Lyu, Senior research scientist and team leader in Sony AI  
    Dr. Nathalie Baracaldo, Research Staff Member at IBM’s Almaden Research Center in San Jose, CA 
    Dr. Virginia Smith, Machine Learning Department at Carnegie Mellon University 
    Dr. Peter Richta´rik, Computer Science at the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia  
    Dr. Zhangyang “Atlas” Wang, Department of Electrical and Computer Engineering, The University of Texas at Austin  
    Dr. Mang Ye, School of Computer Science, Wuhan University, China  
    Dr. Peter Kairouz, Google Research, USA 


Call for paper 

Main research topics of relevance to this workshop include, but are not limited to: 

    Novel FL models for computer vision tasks, e.g., scene understanding, face recognition, object detection, person re-identification, image segmentation, human action recognition, medical image processing, etc. 
    Privacy-preserving machine learning for computer vision tasks 
    Personalized FL models for computer vision applications 
    Novel computer vision applications of FL and privacy-preserving machine learning 
    FL frameworks and tools designed for computer vision applications and benchmarking 
    Novel vision datasets for FL 
    Optimization algorithms for FL, particularly algorithms tolerant of data heterogeneity and resource heterogeneity 
    Approaches that scale FL to larger models, including model pruning and gradient compression techniques 
    Label efficient learning in FL, e.g., self-supervised learning, semi-supervised learning, active learning, etc. 
    Neural architecture search (NAS) for FL 
    Life-long learning in FL 
    Attacks on FL including model poisoning, data poisoning, and corresponding defenses 
    Fairness in FL 
    Federated domain adaptation 
    Privacy leakage and defense in the FL environments 
    Privacy-preserving Generative models for CV  
    FL based CV pipeline for scene understanding and visual analytics 




    Dr. Chen Chen, Assistant Professor, Center for Research in Computer Vision, University of Central Florida 
    Matias Mendieta, Ph.D. Candidate, Center for Research in Computer Vision, University of Central Florida 
    Salman Avestimehr, Professor, University of Southern California, Inaugural Director of the USC-Amazon  Center for Secure and Trusted Machine Learning 
    Zhengming Ding, Assistant Professor, Tulane University 
    Mi Zhang, Associate Professor, Ohio State University 
    Ang Li, Assistant Professor, Department of Electrical and Computer Engineering, University of Maryland (UMD) College Park 
    Bo Li, Associate Professor, Department of Computer Science, University of Chicago 
    Shiqiang Wang, Staff Research Scientist, IBM T. J. Watson Research Center 
    Yang Liu, Associate Professor, Institute for AI Industry Research (AIR), Tsinghua University 
    Gauri Joshi, Associate Professor, Department of Electrical and Computer Engineering, Carnegie Mellon University 
    Saeed Vahidian, Postdoctoral Associate, Department of Electrical and Computer Engineering, Duke University 

Paper (& supplementary material) Submission Deadline: March 11, 2024 (11:59 PM, PST) 

Notification: April 6, 2024 (11:59 PM, PST) 

Camera-Ready: April 14, 2024 (11:59 PM, PST) 

Accepted papers will be published in conjunction with CVPR 2024
proceedings. Paper submissions will adhere to the CVPR 2024 paper
submission style, format, and length restrictions.

The CVPR 2024 author kit is available: 

Paper submission website: 

For any questions, please contact Dr. Chen Chen (