Federated Transfer Learning
An advanced machine learning approach that combines federated learning and transfer learning to train models on decentralized data while leveraging knowledge from pre-trained models, enhancing privacy, reducing data centralization risks, and improving model performance, especially in scenarios where data cannot be shared due to privacy or regulatory concerns.
Areas of application
- Healthcare and Medical Research
- Financial Services and Banking
- Telecommunication Services
- Transportation and Logistics
- Social Networking Services
- Remote Sensing and Satellite Imagery
- Internet of Things (IoT)
- Cybersecurity
Example
A healthcare company uses FTL to train a model on patient data stored across multiple hospitals, while leveraging knowledge from pre-trained models to improve accuracy and reduce the risk of data breaches.