Applied federated learning

From privacy-centric AI in smartphones and vehicles to enhancing machine learning in complex, data-rich environments.

Cross-Device AI

Smartphones

Value

The advancement in AI services demands privacy-preserving mechanisms, especially for applications that operate across various devices like mobiles and customer service systems. These services include personalized recommendations, user behavior analysis, and enhanced user experience without compromising user privacy. The aim is to deliver sophisticated AI features while adhering to strict privacy standards.

Data challenges

Cross-device AI applications typically require the aggregation and analysis of sensitive user data, such as personal preferences, behavior patterns, and communication content. This data is often subject to privacy regulations like GDPR, necessitating strict controls on data sharing and processing. The challenge is to leverage this data for AI without violating privacy norms or exposing sensitive information.

Federated learning

Federated Learning is ideal for this scenario as it allows for the decentralized training of AI models directly on users' devices. This approach ensures that sensitive data remains on the device, reducing the risks associated with data transfer and storage.

It enables the collaborative improvement of AI models while maintaining data privacy and compliance with regulations, thus offering a robust solution for cross-device AI applications.

More use cases

From privacy-centric AI in smartphones and vehicles to enhancing machine learning in complex, data-rich environments.

Cross-Device

Privacy-focused ML for smartphones

Data federations

Data collaborations for unlocking collective value

Medical AI

ML in complex environments

Energy-smart buildings

IoT AI

Fleet Intelligence

Predictive maintenance optimization

Edge learning

Reduce latency and cost in EdgeAI