AI Dictionary
Federated Learning
Definition
A machine learning technique that trains an algorithm across multiple decentralized edge devices or servers.
Deep Dive
Federated Learning is a decentralized machine learning approach that enables AI models to be trained across multiple distributed devices or servers holding local data samples, without exchanging the data itself. Instead of pooling all data into a central location, which can raise significant privacy and security concerns, only the model updates (e.g., gradients or weights) are communicated back to a central server. This allows for collaborative model building while preserving data privacy and sovereignty.
Examples & Use Cases
- 1Mobile keyboards training language models on users' local typing patterns to improve predictive text without sending private input data
- 2Healthcare organizations collaboratively training diagnostic AI models on their diverse patient datasets while keeping sensitive patient records private
- 3IoT devices collectively learning patterns for anomaly detection without sending raw sensor data to a central cloud server
Related Terms
Edge ComputingPrivacy-Preserving AIDistributed Machine Learning