mlsanityData to 14 Oct 2026

Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources

arXiv 2411.16737 ↗First posted 23 Nov 2024cs.LG cs.DC cs.NEcited by 4

Not on the map. Why?

Abstract (arXiv)

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as textttFlower and textttTensorFlow Federated and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 32 of its 85 references that are in the map. First posted on arXiv 23 Nov 2024.

Builds on

32 of 85 references.

References · 32

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  5. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  6. 2020Adaptive Federated Optimizationcited by 1,046
  7. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  9. 2016Quantum Machine Learningcited by 326
  10. 2021Quantum advantage in learning from experimentscited by 188
  11. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  12. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
20 more
  1. 2021Federated Quantum Machine Learningcited by 82
  2. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  3. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  4. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  5. 2023Towards Quantum Federated Learningcited by 20
  6. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  7. 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
  8. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  9. 2021Federated Learning for Industrial Internet of Things in Future Industriescited by 14
  10. 2023Efficient Asynchronous Federated Learning with Sparsification and Quantizationcited by 9
  11. 2021A Hybrid Architecture for Federated and Centralized Learningcited by 8
  12. 2024Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcarecited by 7
  13. 2024FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imagingcited by 5
  14. 2023Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropoutcited by 4
  15. 2024An advanced data fabric architecture leveraging homomorphic encryption and federated learningcited by 4
  16. 2018The New Threats of Information Hiding: the Road Aheadcited by 3
  17. 2018Towards Blockchain-Driven, Secure and Transparent Audit Logscited by 3
  18. 2016Software-Defined Networking-based Crypto Ransomware Detection Using HTTP Traffic Characteristicscited by 2
  19. 2021The Difficulty in Scaling Blockchains: A Simple Explanationcited by 1
  20. 2024A review on different techniques used to combat the non-IID and heterogeneous nature of data in FLcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Molecular Machine Learning in Chemical Process Designcited by 5
  2. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 3
  3. 2025Large Language Models Transform Organic Synthesis From Reaction Prediction to Automationcited by 1
  4. 2026Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimizationcited by 0