Template:Site description
Jump to navigation
Jump to search
Specific informations of {{{1}}}'s site:
- [[{{{1}}}:Hardware|Hardware]]
- [[{{{1}}}:Network|Network]]
- [[{{{1}}}:Storage|Storage]]
- [[{{{1}}}:External access|External access]]
- [[{{{1}}}:People|People]]
- Administration
Direct access to resources for {{{1}}}'s site:
- reservation state and reservation history via OAR
- available resources via Ganglia
- critical services via Nagios
- opened bugs via BugZilla
- {{{1}}}-staff via mail
Shortcuts to global tools and informations:
- support procedures to report bugs or ask for enhancements
- global reservation state and global reservation history via OARgrid.
- registered users via phpLDAPadmin
Latest updated publications Five random publications that benefited from Grid'5000 (at least 2976 overall):
- Eva Giboulot, Teddy Furon. WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off. NeurIPS 2024 - 38th Conference on Neural Information Processing Systems, Dec 2024, Vancouver, Canada. pp.1-34. hal-04766606 view on HAL pdf
- Chih-Kai Huang, Guillaume Pierre. UnBound: Multi-Tenancy Management in Scalable Fog Meta-Federations. UCC 2024 - 17th IEEE/ACM International Conference on Utility and Cloud Computing, Dec 2024, Sharjah, United Arab Emirates. pp.1-11. hal-04760398 view on HAL pdf
- Cédric Prigent, Kate Keahey, Alexandru Costan, Loïc Cudennec, Gabriel Antoniu. On the Reproducibility Challenges of Federated Learning: Investigating the Gap between Simulation, Emulation and Real-World Deployments. CCGrid 2025 - IEEE 25th International Symposium on Cluster, Cloud and Internet Computing, May 2025, Tromso, Norway. pp.185-194, 10.1109/ccgrid64434.2025.00054. hal-04997547 view on HAL pdf
- Cédric Prigent. Towards Efficient and Trustworthy Federated Learning on the Computing Continuum. Machine Learning cs.LG. INSA de Rennes, 2025. English. NNT : 2025ISAR0003. tel-05279213 view on HAL pdf
- Angelo Rodio, Giovanni Neglia, Zheng Chen, Erik G Larsson. A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication. AAAI-26 - 40th Annual AAAI Conference on Artificial Intelligence, Jan 2026, Singapore, Singapore. hal-05423080 view on HAL pdf
Latest updated experiments {{#experiments: 5|{{{1}}}}}
{{{misc}}}