Workshop on Federated Learning for Computer Vision Call for Papers

 Call for Papers: The Fifth Workshop on Federated Learning for Computer Vision (FedVision 2026) @ CVPR 2026
Website: https://fedvision.github.io/fedvision2026/

Overview:

The growing shift from centralized clouds to edge devices is reshaping
AI. Federated Learning (FL) enables large-scale, privacy-preserving
intelligence at the edge, offering unique opportunities and challenges
for computer vision—where data are rich in semantics and
privacy-sensitive. Building on four successful editions at CVPR
2022–2025, FedVision-2026 expands its focus to foundation-model
adaptation, personalized and efficient edge learning, and trustworthy
visual intelligence. This workshop fosters collaboration across
academia, industry, and open-source communities to define the next
frontier of distributed visual learning.

Topics of Interest:
We welcome papers on, but not limited to:

    Foundation-Model-Centric FL: Knowledge distillation, federated
    transfer learning, prompt tuning for vision–language models,
    and optimization for training/adapting foundation models in FL.

    Algorithms and Systems: Device- and data-heterogeneous FL,
    communication and resource efficiency, privacy-preserving
    optimization, label-efficient/self-supervised learning, neural
    architecture search, lifelong/federated domain adaptation, model
    compression, gradient sparsification, and edge deployment.

    Applications and Benchmarks: FL for scene understanding, face
    recognition, object detection, image segmentation, action
    recognition, medical imaging, novel datasets/benchmarks, and
    open-source FL frameworks (e.g., FedML, Flower, OpenFL).  Trust,
    Fairness & Security: Privacy leakage and defenses, model/data
    poisoning attacks and robust defenses, fairness, interpretability,
    machine unlearning, and ethical/societal implications of visual
    data federation.

Important Dates:

    Paper Submission Deadline: March 20, 2026 (11:59 PM PST)
    Notification: April 6, 2026 (11:59 PM PST)
    Camera-Ready: April 11, 2026 (11:59 PM PST)

Accepted papers will be published in conjunction with CVPR 2026
proceedings and must follow the CVPR 2026 paper format.

    Author Kit: https://github.com/cvpr-org/author-kit/releases
    Submission Site: 
 https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FFedVision2026

Organizers:

    Chen Chen, Associate Professor, Center for Research in Computer Vision, Institute of Artificial Intelligence, University of Central Florida, Orlando, FL, USA, chen.chen@crcv.ucf.edu (lead organizer)
    Guangyu Sun, Ph.D. Candidate, Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA, guangyu@ucf.edu
    Nathalie Baracaldo, Research Staff Member, IBM Almaden Research Center, San Jose, CA, USA, baracald@us.ibm.com
    Victor Zhu, Sr. Manager of Research, Axon AI, USA, vzhu@axon.com
    Nicholas Lane, Professor, University of Cambridge, Cambridge, UK, ndl32@cam.ac.uk
    Yang Liu, Associate Professor, HK Polytechnic University, China, yang-veronica.liu@polyu.edu.hk
    Mahdi Morafah, Postdoctoral Researcher, The Wharton School, University of Pennsylvania, USA, mmorafah@wharton.upenn.edu
    Aritra Dutta, Assistant Professor, University of Central Florida, USA, aritra.dutta@ucf.edu
    Zhishuai Guo, Assistant Professor, Northern Illinois University, USA, zguo@niu.edu

For any questions, please contact Dr. Chen Chen at chen.chen@ucf.edu.