Artificial Intelligence
What is Federated Learning
Distributed learning without data transfer
Federated Learning — machine learning approach where the model is trained on distributed data without centralization, preserving privacy.
How it works
- Local training — model trains on user devices
- Gradient aggregation — only model parameters are sent to server
- Model update — global model updates without data access
- Federated averaging — FedAvg algorithm for weight merging
- Differential privacy — adding noise for protection
Advantages
- Data privacy — data never leaves the device
- GDPR compliance — no personal data transfer
- Using distributed data — access to large volumes
- Reduced latency — local processing
- Lower network load — only parameters are transferred
Applications
- Smartphone keyboards — training predictive input
- Healthcare — analyzing data from different clinics
- Finance — joint models without data disclosure
- IoT — training on edge devices
- Vehicles — improving autopilot without video transfer