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

 Full-day workshop at CVPR 2023. June 19th, Vancouver, Canada.

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

in Conjunction with CVPR 2023 

6/19/2023 (Full-Day Workshop)

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.

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 09, 2023 (11:59 PM, PST) 

Notification: March 27, 2023 (11:59 PM, PST) 

Camera-Ready: April 5, 2023 (11:59 PM, PST) 

Accepted papers will be published in conjunction with CVPR 2023
proceedings. Paper submissions will adhere to the CVPR 2023 paper
submission style, format, and length restrictions.

The CVPR 2023 author kit is available: 

Paper submission website: 

For any questions, please contact Dr. Chen Chen (