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
https://fedvision.github.io/fedvision2024/
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
Organizers
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:
https://github.com/cvpr-org/author-kit/releases
Paper submission website: https://cmt3.research.microsoft.com/FEDVISION2024
For any questions, please contact Dr. Chen Chen (chen.chen@crcv.ucf.edu)