Federated learning

Federated learning is a decentralized machine learning approach where models are trained across multiple devices or servers without transferring the raw data to a central location. Instead, only the model updates are shared, maintaining data privacy and security. In the PrivateAI project, federated learning is planned to be implemented to enhance data security, providing additional benefits for users.

• In PrivateAI, models will be trained locally on user devices. This means that the initial data will remain on the local device, reducing the risk of data exposure. The data will never leave the device, ensuring that sensitive information is not transmitted to a central server.
• By keeping data decentralized, federated learning will minimize the chances of unauthorized access. Each device will maintain control over its data, providing an additional layer of security.
• Federated learning will help PrivateAI comply with data protection regulations by keeping sensitive data localized. This decentralized method aligns with legal standards for data privacy and security, aiding in adherence to regulations such as GDPR and HIPAA.
• Federated learning will allow different entities to collaborate on training machine learning models without sharing their raw data. Each participant will contribute to the model training process without exposing their underlying data.
• PrivateAI plans to utilize federated learning to harness the computational power of multiple decentralized nodes. These nodes will perform local computations and periodically send model updates, instead of raw data, to a central server. The server will aggregate these updates to refine the global model, ensuring data integrity and confidentiality throughout the process.
• Federated learning will allow PrivateAI to train models on a diverse range of data from different sources. This will enhance the generalization capability of AI models, making them more effective across various scenarios.