International Workshop on Privacy-Preserving & Collaborative High-Performance Computing
Scientific discovery increasingly depends on AI over large, distributed datasets — yet privacy rules, institutional policy, and the cost of moving data keeps those datasets apart. This workshop brings Federated learning and HPC together: training shared models across institutions without moving raw data.
We welcome original research across the federated-HPC stack. Topics include, but are not limited to, the following six areas.
Early work-in-progress is encouraged alongside full papers — bring it to a forum of leading researchers in the field and build new collaborations.
Up to Minimum 6 Pages (excluding references, figures, tables) and up to 10 pages (including references, figures, tables). double-column IEEE format (as per the conference), including references, appendix and everything.
Each paper receives three reviews. Authors will be asked to raise any concerns in case of LLM-generated reviews, similarly papers will be desk-rejected with either of similarity or AI plagirisim.
An artifact evaluation accompanies the review process to ensure reproducibility, such papers will be further forwarded for consideration of best paper.
The submission site opens by September 1, 2026. Full papers are due October 4, 2026.
Go to submission siteExperts from academia and industry sharing perspectives on federated and high-performance scientific computing.
