A Path Towards Secure Federated Learning

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.