Federated Learning
- [High Tech Park, Tel Aviv, Israel - Shay Weiss]
- Overview
Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets contained in local nodes without explicitly exchanging data samples.
Federated learning is a machine learning technique that allows multiple entities to train a model together without sharing raw data. Instead, data is kept local and only model updates are exchanged through a communication network.
Federated learning offers several advantages, including:
- Data privacy: Federated learning keeps data local, which can help overcome privacy and confidentiality concerns.
- Security: Federated learning can help with security.
- Efficiency: Federated learning can be efficient, especially when communication efficiency is important.
- Scalability: Federated learning can be scalable.
Here are some examples of how federated learning can be used:
- Next-word prediction: Gboard by Google uses federated learning to enhance next-word predictions on mobile keyboards while respecting user privacy.
- Autocorrect and suggestions: Apple's QuickType Keyboard uses federated learning to improve autocorrect and suggestion features on its devices.
- Medical data: Federated learning can be used to aggregate medical data, such as lung scans and brain MRIs, to help detect and treat diseases.
- Customer financial records: Federated learning can be used to aggregate customer financial records to generate more accurate credit scores or detect fraud.
- Car-insurance claims: Federated learning can be used to pool car-insurance claims to improve road and driver safety.
- Factory assembly lines: Federated learning can be used to aggregate sound and image data from factory assembly lines to detect machine breakdowns or defective products.
- Satellite images: Federated learning can be used to aggregate satellite images across countries to improve climate and sea-level rise predictions.
[More to come ...]
