Long papers
Up to 6 pages — eligible for oral presentation
Machine Learning/CORE Unranked
CODEC-FM 2026
NeurIPS Workshop on Collaborative, Open, and Decentralized Training of Foundation Models
CODEC-FM 2026 is a NeurIPS workshop focused on collaborative, open, and decentralized training of foundation models, aiming to enable large-scale model development beyond centralized GPU clusters by leveraging distributed, heterogeneous compute resources—from inter-datacenter setups to internet-scale volunteer collaboration. The workshop brings together researchers to address technical challenges in communication efficiency, fault tolerance, security, and economic models for decentralized AI training.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Tiny papers
Up to 2 pages — poster presentations only
Research areas in scope
01
Topics of interest
Open model development and community infrastructureCommunication-efficient distributed trainingAsynchronous and local-update optimizationInter-datacenter trainingInternet-scale volunteer collaborationFederated learning at scaleHeterogeneous devices and interconnectsFault tolerance, stragglers, and elasticityParallelism strategies over low-bandwidth interconnectsSecurity and robustness in trustless settingsIncentives, governance, and economic modelsBenchmarks and evaluation
Policies worth checking twice
- Submissions are non-archival
- All authors are required to have a valid OpenReview profile
- Submissions are managed through OpenReview
- Reviewing is double blind
- Each submission will receive three reviews
- Organizers follow the NeurIPS conflict-of-interest policy
- Page limits exclude references and appendix
- Use the official NeurIPS 2026 LaTeX style files
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026