The Fourth Workshop on Federated Learning for Computer Vision (FedVision) Call for Papers

 The Fourth Workshop on Federated Learning for Computer Vision (FedVision)
in Conjunction with CVPR 2025
https://fedvision.github.io/fedvision2025/
 
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

 
Keynote speakers

    Dr. Shandong Wu, Associate Professor, Department of Radiology, University of Pittsburgh
    Dr. Shiqiang Wang, Staff Research Scientist, IBM T. J. Watson Research Center, NY, USA
    Dr. Xi Peng, Assistant Professor, Department of Computer & Information Sciences at the University of Delaware
    Dr. Gauri Joshi, Associate Professor, Department of Electrical and Computer Engineering, Carnegie Mellon University
    Salman Avestimehr, Professor, University of Southern California, Inaugural Director of the USC-Amazon  Center for Secure and Trusted Machine Learning
    Dr. Yinzhi Cao, Associate Professor, Department of Computer Science, Johns Hopkins University
    Dr. Xiaoxiao Li, Assistant Professor, Electrical and Computer Engineering Department, the University of British Columbia

 
Organizers
 

    Chen Chen, Associate Professor, Center for Research in Computer Vision, University of Central Florida
    Guangyu Sun, Ph.D. Candidate, Center for Research in Computer Vision, University of Central Florida
    Mahdi Morafah, Ph.D. Candidate, Department of Electrical and Computer Engineering, UCSD
    Nathalie Baracaldo, Research Staff Member at IBM’s Almaden Research Center in San Jose, CA
    Peter Richta´rik, Computer Science at the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
    Mi Zhang, Associate Professor, Ohio State University
    Ang Li, Assistant Professor, Department of Electrical and Computer Engineering, University of Maryland (UMD) College Park
    Nicholas Lane, University of Cambridge and Flower Labs
    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
    Lingjuan Lyu, Senior research scientist and team leader in Sony AI

 
Paper (& supplementary material) Submission Deadline: March 15, 2025 (11:59 PM, PST)
Notification: April 1, 2025 (11:59 PM, PST)
Camera-Ready: April 6, 2025 (11:59 PM, PST)
Accepted papers will be published in conjunction with CVPR 2025
proceedings. Paper submissions will adhere to the CVPR 2025 paper
submission style, format, and length restrictions.

The CVPR 2025 author kit is available: 
https://github.com/cvpr-org/author-kit/releases

Paper submission website: https://cmt3.research.microsoft.com/FedVision2025
 
For any questions, please contact Dr. Chen Chen (chen.chen@crcv.ucf.edu)