Definition: Federated learning is a machine learning technique that allows multiple clients to collaboratively train a shared model without sharing their raw data.

Detailed Description:

In federated learning, clients train local models on their own data, and these models are then aggregated to create a global model. This approach preserves data privacy while still enabling the development of powerful machine learning models.

Federated learning is particularly useful for applications where data privacy is a concern, such as healthcare and mobile devices.

Related Terms:

• Machine Learning
• Distributed Learning
• Privacy-Preserving Machine Learning
• Data Privacy

FAQs:

1. How does federated learning work?

   • Clients train local models on their own data, and the models are aggregated to create a global model. The global model is then shared with clients, who can use it to make predictions.

2. What are the benefits of federated learning?

   • Federated learning preserves data privacy, improves model performance, and enables collaboration between multiple organizations.

3. What are the challenges of federated learning?

   • Challenges include communication overhead, data heterogeneity, and ensuring model convergence.