Published March 13, 2026
| Version 138c362
Software
Open
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
Authors/Creators
- 1. Samsung R&D Institute UK (SRUK)
- 2. Information Technologies Institute (CERTH-ITI)
Description
Artifact archive for "DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning", MLSys 2026.
Files
mlsys26_disagg-main.zip
Files
(6.5 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:4adebcd5c7c501171acae6881752cb4c
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6.5 MB | Preview Download |
Additional details
Software
- Repository URL
- https://github.com/SamsungLabs/mlsys26_disagg
- Programming language
- Python
References
- Mehmood, H., Tatsis, G., Alexopoulos, D., Saravanan, K., Xu, J., Drosou, A., and Ozay, M. DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning. To appear in Proceedings of the Ninth Annual Conference on Machine Learning and Systems, MLSys 2026.