# Federated Data Science to Break Down Silos [Vision]

arXiv: https://arxiv.org/abs/2111.13186 · HTML: https://mlsanity.com/paper/2111.13186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Learning Deep Generative Models of Graphs](https://mlsanity.com/paper/1803.03324/) (2018)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning](https://mlsanity.com/paper/2010.16103/) (2020)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [Message Passing for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2009.10847/) (2020)
- [Leveraging Abstract Meaning Representation for Knowledge Base Question Answering](https://mlsanity.com/paper/2012.01707/) (2020)
- [ProGraML: Graph-based Deep Learning for Program Optimization and Analysis](https://mlsanity.com/paper/2003.10536/) (2020)
- [A Toolkit for Generating Code Knowledge Graphs](https://mlsanity.com/paper/2002.09440/) (2020)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (2021)
- [Semantic Annotation for Tabular Data](https://mlsanity.com/paper/2012.08594/) (2020)
- [A comparative study of similarity-based and GNN-based link prediction approaches](https://mlsanity.com/paper/2008.08879/) (2020)

## Built on by (most cited first, 1 of 1)

- [KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science](https://mlsanity.com/paper/2303.02204/) (2023)

## Abstract (arXiv)

Similar to Open Data initiatives, data science as a community has launched initiatives for sharing not only data but entire pipelines, derivatives, artifacts, etc. (Open Data Science). However, the few efforts that exist focus on the technical part on how to facilitate sharing, conversion, etc. This vision paper goes a step further and proposes KEK, an open federated data science platform that does not only allow for sharing data science pipelines and their (meta)data but also provides methods for efficient search and, in the ideal case, even allows for combining and defining pipelines across platforms in a federated manner. In doing so, KEK addresses the so far neglected challenge of actually finding artifacts that are semantically related and that can be combined to achieve a certain goal.