Federated Learning
Training AI models across distributed devices without centralizing data, preserving privacy while enabling collaborative learning.
Definition
Federated learning enables multiple organizations or devices to collaboratively train AI models without sharing raw data. Each participant trains on their local data and shares only model updates, not the data itself.
This approach allows organizations to benefit from larger, diverse datasets while maintaining data privacy and complying with regulations. The central server aggregates updates to create a global model that all participants can use.
Why It Matters
Data privacy regulations and competitive concerns often prevent organizations from sharing data, limiting AI model quality. Federated learning enables collaboration while maintaining data sovereignty and privacy.
This approach unlocks new possibilities for industry-wide AI improvements, allowing competitors to collaborate on common challenges while protecting proprietary information and maintaining regulatory compliance.
Examples in Practice
Healthcare networks use federated learning to develop diagnostic models across hospitals without sharing sensitive patient data, creating more robust models that work across diverse populations.
Financial institutions collaborate on fraud detection models through federated learning, sharing insights about fraudulent patterns without revealing customer transaction details.
Smartphone manufacturers employ federated learning to improve predictive text and voice recognition by training on user interactions across millions of devices without collecting personal communications.