FedMedW 2023 : Workshop on Federated Learning in Medical Imaging and Vision in conjunction with International Conference on IMAGE ANALYSIS AND PROCESSING

--- Call for papers - Apologies for multiple posting ---

1st Workshop on Federated Learning in Medical Imaging and Vision

Held in conjunction with the “22nd International Conference on Image Analysis and Processing (ICIAP)” - September 11-15, 2023, Udine (Italy)

Web site: https://fedmedw.github.io/


About

ICIAP 2023 is the 22nd edition of a biennial series of conferences by CVPL, the Italian Member Society of the International Association for Pattern Recognition (IAPR). The conference focuses on classic and recent trends in computer vision, pattern recognition, and image processing and covers theoretical and applicative aspects. The event is hosted by the University of Udine, located in the downtown center of Udine. Udine is located in Friuli Venezia Giulia region, in the North East of Italy. Udine is about 140 km from Venice and 80 km from Trieste.

The 1st Workshop on Federated Learning (FL) in Medical Imaging and Vision aims to bring together researchers and practitioners with a common interest in FL for visual tasks, focusing on medical imaging, to address this research area's open questions and challenges.


Topics
The workshop aims to attract novel and original contributions exploring federated and collaborative learning with its challenges and peculiarities. Expected submissions should cover, but are not limited to, the following topics:

Novel approaches of Federated, Distributed, and Collaborative Learning in medical imaging applications
Topologies: Server-centric, peer-to-peer, cyclic, swarm learning, etc.
Decentralized Learning
Dealing with heterogeneous and unbalanced (non-IID) data distributions
Security and privacy of FL systems
Personalized FL models
New Datasets for FL
Optimization methods for distributed and collaborative learning
Adversarial, inversion, back-dooring, and other forms of attacks on distributed and federated learning
Model sharing techniques
Novel applications of FL techniques: image classification, segmentation, reconstruction, regression; multi-task learning, model agnostic learning, meta-learning, unsupervised
Applications of federated, distributed, and collaborative learning techniques in the medical field.
Explainability and interpretability in FL
Federated continual learning
Asynchronous FL

Important dates
Paper submission deadline: June 30, 2023

Notification to authors: July 30, 2023

Camera-ready deadline: August 15, 2023



Proceedings


Accepted papers will be published in Springer LNCS in a separate proceedings book.



Organizers
Simone Palazzo (University of Catania)

Federica Proietto Salanitri (University of Catania)

Matteo Pennisi (University of Catania)

Chen Chen (University of Central Florida)

Bruno Casella (University of Turin)

Gianluca Mittone (University of Turin)