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)