Federated Learning

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Did you know? By 2027, the federated learning market is projected to reach $210 million, growing rapidly due to rising data privacy demands.

1. What is federated learning?

Federated learning is a machine learning technique where multiple devices or servers collaboratively train a shared model without sharing raw data. Each participant trains locally, and only the model updates are aggregated; enhancing privacy and security.

2. Difference between federated learning and machine learning?

• Traditional machine learning collects all data centrally for training.
  
• Federated learning keeps data decentralized, training models directly on user devices or edge nodes.
  This protects privacy and reduces the risk of data breaches.
  

3. What are the three types of federated learning?

The three types of federated learning are:

1. Horizontal FL – different users with the same feature space but different samples (e.g., hospitals).
   
2. Vertical FL – same users, different feature sets (e.g., banks & retailers).
   
3. Federated Transfer Learning – different users and feature sets, requiring transfer learning to bridge gaps.
   

Discover how federate learning and NPL enable secure, real time sentiment insights from customers conversations