Intel Tiber Secure Federated AI Improved Privacy & Security

What is Intel Tiber Secure Federated AI?

A turnkey solution called Intel Tiber Secure Federated AI is made to safely use federated learning to train AI models on private data. Regardless of whether data is stored on-site, in the public cloud, or in a private cloud, it helps guarantee that the data owner always has custody of the data. To assist guarantee high levels of privacy and security for both models and data, the service makes use of hardware-based security, cryptographic approaches, and algorithmic methodologies.

What is Federated Learning?

A machine learning method called federated learning (FL) allows AI models to be trained without relocating them across several dispersed servers or devices that store local data samples. Federated training enables the model to be trained locally on each device rather than transferring data to a central server; just the model updates are shared and combined to enhance the overall model. This approach provides improved intellectual property rights, helps guarantee adherence to data sovereignty regulations, and steadily maintains data security and privacy.

What distinguishes this product from OpenFL?

OpenFL, an open source federated learning framework created by Intel as a component of the Linux Foundation LF AI and Data project, serves as the foundation for Intel Tiber Secure Federated AI. OpenFL is the first federated learning framework authorized for use on the International Space Station and has been widely adopted by sectors including healthcare, insurance, and pharmaceuticals.

Intel Tiber Secure Federated AI gives the clients two main benefits by offering a turnkey deployment of OpenFL:

• Simplified configuration: Offers an easy-to-follow setup procedure that lessens the time and complexity needed to set up federated learning environments.

• Improved security features: Uses zero-trust security protocols to safeguard private information and intellectual property.

Intel Tiber Secure Federated AI

Boost model accuracy while safeguarding private information and intellectual property.

A Complete Federated Learning Solution for AI Model Training on Private Data Is Currently in Beta

To develop strong and broadly applicable AI models, builders need a variety of real-world datasets, yet obtaining datasets based on sensitive and private information is challenging due to privacy constraints. Federated learning provides a solution, but scaling, managing, deploying, and operating the architecture can be challenging.

Because of these difficulties, Intel created Intel Tiber Secure Federated AI, a turnkey solution for safely employing federated learning to train AI models on private data.

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• Boost model accuracy while safeguarding private information and intellectual property.
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Advantages of Product

To assist guarantee high levels of security for both models and data, Intel Tiber Secure Federated AI makes use of algorithmic approaches, cryptography, and hardware-based security. The solution gives to clients a number of significant benefits by offering a complete OpenFL setup.

• Improved Privacy and Security: Uses zero-trust security techniques to safeguard private information and intellectual property.

• Enhanced Accuracy of the Model: To improve the quality and generalizability of your AI products, train them on a bigger and more varied dataset.

• Operational Efficiency: Reduce data redaction and duplication to cut expenses.

• Regulatory Compliance: Implement access control measures to monitor who has access to and uses your data.

Building On the Foundation of OpenFL

OpenFL, an open source federated learning platform, serves as the foundation for Intel Tiber Secure Federated AI.

Federated training enables model training locally on each device, with only the model changes being shared and aggregated to enhance the overall model, as opposed to sending data to a central server. This protects intellectual property, complies with data sovereignty rules, and maintains data privacy and security.

The only federated learning framework authorized for use aboard the International Space Station is OpenFL, which has been extensively utilized in numerous industries.

Use Cases

Collaborative Medical Research

In order to better understand, predict, and prevent a variety of diseases and illnesses, healthcare professionals, hospitals, clinics, and health research organizations have analyzed millions of patient datasets using AI and machine learning models.

AI/ML models can be trained using Intel Tiber Secure Federated AI, which enables several parties to participate to the model by applying the algorithm on the data. While hospitals and clinics retain control over sensitive patient data, this helps to enhance therapeutic outcomes because AI models are more reliable and generalizable.

Early Drug Discovery

The process of finding and creating new treatments is resource-intensive and calls for specific domain knowledge. The performance and generalizability of generative machine learning models, which have become effective tools for drug discovery, rely significantly on data that is frequently fragmented among several research organizations and businesses.

A more robust model could result from combining this data to capture a more thorough and representative distribution. However, technical limitations, pressure from competitors, and privacy and other legal issues make this impractical.

These models may be trained without merging datasets using Intel Tiber Secure Federated AI. This allows businesses to safely work together on model training while addressing data privacy issues.

Fraud Identification

Real-time fraud detection using AI and machine learning is becoming more popular, but many small and medium-sized banks lack the amount of transaction data required to build a reliable detection model. Although several banks might combine their fraud data, regulatory issues prevent them from doing so.

Without transferring data, fraud detection models may be safely trained across several banks with Intel Tiber Secure Federated AI. This can result in more accurate fraud and less losses.

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