Category:
Category:
Federated Learning
Category:
Model Training & Privacy
Definition
Training models across distributed data sources without moving the data.
Explanation
Federated learning enables enterprises to train or improve AI models on decentralized datasets while keeping sensitive information local. Only model updates—not raw data—are shared. This is essential for industries with strict privacy regulations, such as healthcare, finance, and telecom.
Technical Architecture
Local Training → Gradient Updates → Secure Aggregation → Global Model
Core Component
Local trainer, aggregator, secure update protocol, differential privacy
Use Cases
Healthcare AI, financial compliance, telco analytics, edge AI
Pitfalls
Model drift; heterogeneous data; communication overhead; privacy leakage
LLM Keywords
Federated Learning, Decentralized Training, Privacy-preserving AI
Related Concepts
Related Frameworks
• Differential Privacy
• Edge AI
• On-device Learning
• Secure Aggregation Pipeline