# Federated Automated Feature Engineering

arXiv: https://arxiv.org/abs/2412.04404 · HTML: https://mlsanity.com/paper/2412.04404/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering](https://mlsanity.com/paper/2305.03403/) (2023)
- [The autofeat Python Library for Automated Feature Engineering and Selection](https://mlsanity.com/paper/1901.07329/) (2019)
- [OpenFE: Automated Feature Generation with Expert-level Performance](https://mlsanity.com/paper/2211.12507/) (2022)
- [DIFER: Differentiable Automated Feature Engineering](https://mlsanity.com/paper/2010.08784/) (2020)
- [Hybrid Federated Learning: Algorithms and Implementation](https://mlsanity.com/paper/2012.12420/) (2020)
- [Endgame Analysis of Dou Shou Qi](https://mlsanity.com/paper/1604.07312/) (2016)
- [A Primal-Dual Algorithm for Hybrid Federated Learning](https://mlsanity.com/paper/2210.08106/) (2022)
- [IIFE: Interaction Information Based Automated Feature Engineering](https://mlsanity.com/paper/2409.04665/) (2024)
- [Comprehensive Performance Analysis of Homomorphic Cryptosystems for Practical Data Processing](https://mlsanity.com/paper/2202.02960/) (2022)
- [FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework](https://mlsanity.com/paper/2009.02557/) (2020)
- [Aggregation Delayed Federated Learning](https://mlsanity.com/paper/2108.07433/) (2021)
- [Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network](https://mlsanity.com/paper/2404.09443/) (2024)

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

- [Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions](https://mlsanity.com/paper/2503.03140/) (2025)

## Abstract (arXiv)

Automated feature engineering (AutoFE) is used to automatically create new features from original features to improve predictive performance without needing significant human intervention and domain expertise. Many algorithms exist for AutoFE, but very few approaches exist for the federated learning (FL) setting where data is gathered across many clients and is not shared between clients or a central server. We introduce AutoFE algorithms for the horizontal, vertical, and hybrid FL settings, which differ in how the data is gathered across clients. To the best of our knowledge, we are the first to develop AutoFE algorithms for the horizontal and hybrid FL cases, and we show that the downstream test scores of our federated AutoFE algorithms is close in performance to the case where data is held centrally and AutoFE is performed centrally.