mlsanityData to 7 Oct 2026

Federated Automated Feature Engineering

arXiv 2412.04404 ↗First posted 5 Dec 2024cs.LG cs.DCcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 5 Dec 2024.

Builds on

15 of 34 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  4. 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 125
  5. 2019The autofeat Python Library for Automated Feature Engineering and Selectioncited by 73
  6. 2022OpenFE: Automated Feature Generation with Expert-level Performancecited by 52
  7. 2020DIFER: Differentiable Automated Feature Engineeringcited by 16
  8. 2020Hybrid Federated Learning: Algorithms and Implementationcited by 14
  9. 2016Endgame Analysis of Dou Shou Qicited by 11
  10. 2022A Primal-Dual Algorithm for Hybrid Federated Learningcited by 4
  11. 2024IIFE: Interaction Information Based Automated Feature Engineeringcited by 4
  12. 2022Comprehensive Performance Analysis of Homomorphic Cryptosystems for Practical Data Processingcited by 3
3 more
  1. 2020FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Frameworkcited by 2
  2. 2021Aggregation Delayed Federated Learningcited by 2
  3. 2024Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural networkcited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Task-Agnostic Federation over Decentralized Data: Research Landscape and Visionscited by 0