mlsanityData to 7 Oct 2026

Industry-Scale Orchestrated Federated Learning for Drug Discovery

arXiv 2210.08871 ↗First posted 17 Oct 2022cs.LG stat.MLcited by 19

Not on the map. Why?

Abstract (arXiv)

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant ndeg831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 14 of its 49 references that are in the map. First posted on arXiv 17 Oct 2022.

Builds on

14 of 49 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  6. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  7. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  8. 2021Federated Learning: Opportunities and Challengescited by 83
  9. 2017Knock Knock, Who's There? Membership Inference on Aggregate Location Datacited by 63
  10. 2014Multi-task Neural Networks for QSAR Predictionscited by 17
  11. 2020Quality Inference in Federated Learning with Secure Aggregationcited by 15
  12. 2022SparseChem: Fast and accurate machine learning model for small moleculescited by 6
2 more
  1. 2017Together or Alone: The Price of Privacy in Collaborative Learningcited by 4
  2. 2022Collaborative Drug Discovery: Inference-level Data Protection Perspectivecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 6; 2026: 4.

19 papers.

Sort:

  1. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  2. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  3. 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
  4. 2024Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gaincited by 5
  5. 2023FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participantscited by 4
  6. 2024Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacycited by 3
  7. 2024Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sourcescited by 3
  8. 2026FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuningcited by 3
  9. 2023FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settingscited by 2
  10. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  11. 2025Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Featurescited by 1
  12. 2025Federated Instrumental Variable Analysis via Federated Generalized Method of Momentscited by 1
7 more
  1. 2025On the Fragility of Contribution Score Computation in Federated Learningcited by 1
  2. 2025Improving Early Sepsis Onset Prediction Through Federated Learningcited by 1
  3. 2026FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessmentcited by 1
  4. 2024Recovering Global Data Distribution Locally in Federated Learningcited by 0
  5. 2025FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcarecited by 0
  6. 2026A Comparative Study of QSPR Methods on a Unique Multitask PAMPA datasetcited by 0
  7. 2026Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selectioncited by 0