Federated Learning
term_id: federated_learning
Category: ethics_safety
Definition
Federated learning enables organizations to collaboratively train AI models without sharing sensitive raw data. Instead of centralizing information, the model is sent to local devices where it learns from local data, and only model updates (gradients) are transmitted back to a central server for aggregation. This enhances privacy and security, making it ideal for healthcare and finance applications where data sovereignty is paramount.
Summary
Federated learning is a distributed machine learning approach that trains models across decentralized devices while keeping data local.
Key Concepts
- Data privacy
- Distributed training
- Model aggregation
- Edge computing
Use Cases
- Predictive text keyboards
- Medical diagnosis collaboration
- Financial fraud detection