Generating unique molecules with biochemically desired properties to serve as viable drug candidates is a difficult task that requires specialized domain expertise. In recent years, diffusion models have shown promising results in accelerating the drug design process through AI-driven molecular generation. However, training these models requires massive amounts of data, which are often isolated in proprietary silos. OpenFL is a federated learning framework that enables privacy-preserving collaborative training across these decentralized data sites. In this work, we present a federated discrete denoising diffusion model that was trained using OpenFL. The federated model achieves comparable performance with a model trained on centralized data when evaluating the uniqueness and validity of the generated molecules. This demonstrates the utility of federated learning in the drug design process. OpenFL is available at: https://github.com/securefederatedai/openfl

Open Federated Learning (OpenFL) is a deep learning framework agnostic library for federated learning developed at Intel that lets developers train statistical models on sharded datasets, distributed across several nodes (if you are new to OpenFL, refer to the OpenFL medium article). With the release of OpenFL 1.3, we incorporated a lot of exciting features such as flexible task assignment in the interactive API, new support and examples for Huggingface transformers, Pytorch Lightning, MXNet and Numpy, and new aggregation algorithms like FedCurv, FedYogi and FedAdam. But today we focus on a new dimension for our framework: bringing together hardware and software for privacy preserving AI using Intel Software Guard Extensions (Intel SGX) and Gramine. OpenFL was created to address the challenge of maintaining data privacy while bringing together insights from many disparate, confidential or regulated datasets. However, training a model this way introduces new challenges around IP and how it gets used.

OpenFL is an open-source framework for Federated Learning (FL) developed at Intel. FL is a technique for training statistical models on sharded datasets, distributed across several nodes. Moreover, data may be not identically distributed between different shards and cannot be moved between nodes, due to privacy / legal concerns (laws such as HIPAA or GDPR), size of the dataset, or other reasons. OpenFL is designed to solve so-called cross-silo federated learning problems when data is split between organizations or remote data centers. OpenFL aims to provide an effective and secure infrastructure for data scientists. With the v1.2 update OpenFL team endeavors to raise the framework's learnability and decouple the procedure of setting up the Federation and using it to run FL experiments.

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.