Introduction

Opening Remarks, NVIDIA FLARE Overview & Roadmap

NVIDIA AV Team

Autonomous Driving Federated Learning

To enable the training of AV models with combined CN+US multimodal raw sensor data, we proposed the NV-patented Round Robin Federated Learning (RRFL) and integrated it with the NVFLARE framework.

Roche

Unlocking patient-level data at scale with federated computing to drive collaborative research and advance science

We'll present our exploration outcomes of Federated Analysis and Federated Learning in Personalized Healthcare to advance multicenter RWE studies and accelerate scientific discoveries.

Royal Bank of Canada

Confidential Federated Learning in a multi-party environment for product recommendations

By combining data mesh workflows with NVIDIA FLARE, product recommendations can be traced back to the specific data used for training, enabling business stakeholders to verify the fitness for business and regulatory compliance.

Rhino Health

Federated Computing In The Real World: From Drug Discovery to Fraud Detection

In this talk we will discuss some of the challenges we encountered at Rhino Health when working on real-world federated computing projects, as well as some tips for overcoming these challenges, highlighting some projects and use cases that were executed on the Rhino Federated Computing Platform.

Flower Lab

Running Flower Projects on NVFLARE

Join us for an exciting technical talk on the groundbreaking integration between two leading federated learning frameworks: Flower and NVFlare. In this talk, we will show how the integration works, how you can use it, and what's next for Flower-on-Flare.

Apheris

Federated data collaboration in BioPharma: leveraging NVFlare and BioNeMo

This talk will focus on how computational governance, with NVFlare at its heart, revolutionizes federated training and evaluation. We'll present a practical case study of a BioNeMo implementation on Apheris' product, highlighting effective strategies for governance, privacy, and security.

University of Wisconsin

Federated Learning in Medical Imaging: Enhancing Data Privacy and Advancing Healthcare

In this talk, we will discuss the primary motivations for adopting federated learning in medical imaging, delve into successful examples and their impact on improving diagnostic accuracy and patient outcomes, and envision the transformative potential to revolutionize personalized medicine, cross-institutional collaborations, and development of robust AI models.

Oak Ridge National Lab

Towards a Federating Learning Platform for HPC User Facilities

An overview of the ongoing effort to integrate NVFlare into the Oak Ridge Leadership Computing Facilities (OLCF) infrastructure to enable domain agnostic Federated Learning campaigns for HPC.

Deloitte

FedRAG – A Secure Approach to Scaling Retrieval Augmented Generation

In this presentation, we introduce FedRAG, a new privacy-preserving solution for scaling RAG across a decentralized network of data providers. We also demonstrate how to implement FedRAG using the NVIDIA FLARE SDK and cloud-based trusted execution environments.

OPTIMA

Enabling Precision Oncology with Federated Learning: Insights from the OPTIMA Consortium

Lisa Schneider has taken on a pivotal role in implementing Federated Learning within the OPTIMA Consortium. OPTIMA (Optimal Treatment for Patients with Solid Tumours in Europe Through Artificial intelligence) aims to advance treatments and improve decision-making processes for physicians and patients with prostate, breast and lung cancer using machine learning.

FLARE Getting Started and LLM Support

End-to-end Pythonic APIs, LLM support - PEFT, SFT, RAG

Secure XGBoost

Using Homomorphic Encryption for Secure Federated XGBoost Learning

Confidential Computing and Looking Forward

How FLARE integrates with Confidential Computing, Closing Remarks