# Industry-Scale Orchestrated Federated Learning for Drug Discovery

arXiv: https://arxiv.org/abs/2210.08871 · HTML: https://mlsanity.com/paper/2210.08871/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Knock Knock, Who's There? Membership Inference on Aggregate Location Data](https://mlsanity.com/paper/1708.06145/) (2017)
- [Multi-task Neural Networks for QSAR Predictions](https://mlsanity.com/paper/1406.1231/) (2014)
- [Quality Inference in Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2007.06236/) (2020)
- [SparseChem: Fast and accurate machine learning model for small molecules](https://mlsanity.com/paper/2203.04676/) (2022)
- [Together or Alone: The Price of Privacy in Collaborative Learning](https://mlsanity.com/paper/1712.00270/) (2017)
- [Collaborative Drug Discovery: Inference-level Data Protection Perspective](https://mlsanity.com/paper/2205.06506/) (2022)

## Built on by (most cited first, 19 of 19)

- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape](https://mlsanity.com/paper/2405.03636/) (2024)
- [Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning](https://mlsanity.com/paper/2410.19321/) (2024)
- [Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain](https://mlsanity.com/paper/2405.09409/) (2024)
- [FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants](https://mlsanity.com/paper/2312.11391/) (2023)
- [Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources](https://mlsanity.com/paper/2411.16737/) (2024)
- [Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy](https://mlsanity.com/paper/2410.16975/) (2024)
- [Federated Learning from Molecules to Processes: A Perspective](https://mlsanity.com/paper/2506.18525/) (2025)
- [FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning](https://mlsanity.com/paper/2603.08014/) (2026)
- [FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings](https://mlsanity.com/paper/2311.16984/) (2023)
- [Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features](https://mlsanity.com/paper/2505.09287/) (2025)
- [Federated Instrumental Variable Analysis via Federated Generalized Method of Moments](https://mlsanity.com/paper/2505.21012/) (2025)
- [On the Fragility of Contribution Score Computation in Federated Learning](https://mlsanity.com/paper/2509.19921/) (2025)
- [Improving Early Sepsis Onset Prediction Through Federated Learning](https://mlsanity.com/paper/2509.20885/) (2025)
- [FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment](https://mlsanity.com/paper/2602.20194/) (2026)
- [Recovering Global Data Distribution Locally in Federated Learning](https://mlsanity.com/paper/2409.14063/) (2024)
- [FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare](https://mlsanity.com/paper/2504.10817/) (2025)
- [A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset](https://mlsanity.com/paper/2605.00508/) (2026)
- [Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection](https://mlsanity.com/paper/2606.16868/) (2026)

## 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.