# Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning

arXiv: https://arxiv.org/abs/1910.11567 · HTML: https://mlsanity.com/paper/1910.11567/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [A Survey on Efficient Federated Learning Methods for Foundation Model Training](https://mlsanity.com/paper/2401.04472/) (2024)
- [Federated Multilingual Models for Medical Transcript Analysis](https://mlsanity.com/paper/2211.09722/) (2022)
- [Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications](https://mlsanity.com/paper/2304.12012/) (2023)
- [Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning](https://mlsanity.com/paper/2409.16495/) (2024)
- [FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning](https://mlsanity.com/paper/2501.19091/) (2025)
- [FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings](https://mlsanity.com/paper/2311.16984/) (2023)
- [Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework](https://mlsanity.com/paper/2409.13473/) (2024)
- [Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis](https://mlsanity.com/paper/2609.15681/) (2026)
- [A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks](https://mlsanity.com/paper/2308.05014/) (2023)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [Where is the Testbed for my Federated Learning Research?](https://mlsanity.com/paper/2407.14154/) (2024)
- [Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study](https://mlsanity.com/paper/2609.36001/) (2026)

## Abstract (arXiv)

Machine learning is promising, but it often needs to process vast amounts of sensitive data which raises concerns about privacy. In this white-paper, we introduce Substra, a distributed framework for privacy-preserving, traceable and collaborative Machine Learning. Substra gathers data providers and algorithm designers into a network of nodes that can train models on demand but under advanced permission regimes. To guarantee data privacy, Substra implements distributed learning: the data never leave their nodes; only algorithms, predictive models and non-sensitive metadata are exchanged on the network. The computations are orchestrated by a Distributed Ledger Technology which guarantees traceability and authenticity of information without needing to trust a third party. Although originally developed for Healthcare applications, Substra is not data, algorithm or programming language specific. It supports many types of computation plans including parallel computation plan commonly used in Federated Learning. With appropriate guidelines, it can be deployed for numerous Machine Learning use-cases with data or algorithm providers where trust is limited.