Exploring cutting-edge methods, applications, and challenges for Federated Learning in industrial and manufacturing settings.
The integration of Federated Learning (FL) into manufacturing represents a transformative approach to harnessing distributed data while preserving privacy. This special session aims to explore cutting-edge methods, applications, and challenges associated with the implementation of FL in industrial settings.
Bringing together researchers and practitioners, the session addresses topics such as collaborative model training across multiple manufacturing units, dealing with data heterogeneity, ensuring data security, and enhancing applications such as predictive maintenance with FL. Discussions will also cover the role of FL in Industry 4.0, with an emphasis on human-centric and sustainable manufacturing processes.
Topics include, but are not limited to:
All submissions must use the A4 IEEE Manuscript Template for Conference Proceedings (.pdf format). Please include keywords with your submission.
Full research contributions. Overlength papers rejected without review.
Work-in-progress, demos, and artifact papers.
Undergraduate and early-stage research.
Papers must be original work not simultaneously under review elsewhere. Prior work must be cited appropriately. IEEE's plagiarism policy applies.
The final author list must be confirmed before the submission deadline. No changes are permitted afterward.
Accepted, registered, and presented papers will be submitted to IEEE Xplore for possible publication.