Introducing Intel® Tiber™ Secure Federated AI

Revolutionizing AI Model Training with Zero-Trust Security

Intel® Tiber™ Trust Services offers a suite of products designed to protect and fortify enterprise and cloud assets, including hardware, software, data, and AI models. The portfolio is tailored for multi-vendor and multi-cloud environments, enables diverse software consumption models, and delivers robust solutions to fortify security posture and ensure compliance with regulations.

I am excited to announce the beta availability of Intel® Tiber™ Secure Federated AI, a turnkey service designed to securely train AI models on private data using federated learning. This innovative solution overcomes the challenges of obtaining diverse, real-world datasets while trying to ensure data privacy and security – with the simplicity of a turnkey service.

The service is built on our successes and learnings from OpenFL, an open-source federated learning framework developed by Intel as part of the Linux Foundation LF AI and Data project. Instead of sending data to a central server, federated training allows model training locally on each device, with only the model updates being shared and aggregated to improve the overall model. This helps preserve data privacy and security, comply with data sovereignty requirements, and protect intellectual property.

OpenFL has been deployed to train a brain tumor segmentation model in real-world federations, including the world’s largest healthcare federation across 71 sites on six continents. OpenFL is widely used across industries like Insure-Tech, Pharma, and Healthcare, and is the only federated learning framework approved for use on the International Space Station. We continue to invest in OpenFL, bringing advances in federated learning to researchers and universities worldwide.

While OpenFL is an excellent solution for research settings, enterprise customers require an easy-to-use turnkey solution that is straightforward to deploy, manage, operate, and scale – Intel Tiber Secure Federated AI addresses these needs. It is designed using Zero-Trust Security principles, facilitating ease of use and ensures that data stays in the owner’s custody no matter where it is stored – on-premises, public cloud, or private cloud. It provides the following key benefits:

• Simplified Configuration: Provides a user-friendly setup process that helps reduce the complexity and time required to establish federated learning environments.

• Enhanced Security and Privacy: Implements zero-trust security measures designed to protect sensitive data and model intellectual property.

• Improved Model Accuracy: Trains the AI products on a larger and more diverse dataset to enhance their quality and generalizability.

• Operational Efficiency: Helps control costs by reducing data duplication and redaction efforts.

• Regulatory Compliance: Designed to control who uses your data and how it is used to adhere to regulations.

Some key use cases the solution is well suited for include:

• Collaborative medical research: Train AI/ML models on patient data across multiple healthcare providers while maintaining control of sensitive information. 

• Early drug discovery: Securely collaborate on model training with data from different research institutions and companies without combining datasets. Abhishek Pandey, Global Lead and Principal Research Scientist II at AbbVie, says: “No one pharmaceutical company has the quantity and diversity of data in every molecular modality. The only way to build robust algorithms is to use secure federated AI to train models across private data (silos) that benefits all pharmaceutical companies while protecting our high-value data. Our collaboration with Intel is a great step towards advancing this goal.” 

• Fraud detection: Train fraud detection models across multiple banks without moving data, enhancing accuracy and reducing losses. 

To learn more, join us at HIMSS25 in Las Vegas March 3–6, at the Dell Technologies booth (#4317) where Intel, Harvard Medical School, and Dell Technologies will talk about the use of federated AI technology in healthcare and other areas.

For more information, visit intel.com/secfedai or contact us at SecureFederatedAI@intel.com to learn more about how you can access our service.

Purnam Sheth is the Intel Vice President and General Manager for Trust and Security Products.