Federated Learning for Computer Vision (FedVision) Call for Papers
CALL FOR PAPERS & CALL FOR PARTICIPANTS
CVPR 2022 Workshop: Federated Learning for Computer Vision (FedVision)
(In conjunction with CVPR 2022, June 19, New Orleans, US)
Website: https://sites.google.com/view/fedvision
Contact: Dr. Chen Chen (chen.chen@crcv.ucf.edu)
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. Yiran Chen, Duke University
Dr. Salman Avestimehr, University of Southern California
Dr. Zheng Xu, Google
Dr. Jeffrey Byrne, CEO of Visym Labs
Dr. Handong Zhao, Adobe System
Dr. Yuejie Chi, Carnegie Mellon University
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
Submission Deadline: March 9, 2022 (11:59 PM, PST)
Notification: March 27, 2022 (11:59 PM, PST)
Camera-Ready: April 5, 2022 (11:59 PM, PST)
Accepted papers will be published in conjunction with CVPR 2022
proceedings. Paper submissions will adhere to the CVPR 2022 paper
submission style, format, and length restrictions. The CVPR 2022
author kit is available:
https://cvpr2022.thecvf.com/sites/default/files/2021-10/cvpr2022-author_kit-v1_1-1.zip
Enter the paper submission website:
https://cmt3.research.microsoft.com/FedVision2022
For any questions, please contact Dr. Chen Chen (chen.chen@crcv.ucf.edu)