← 🛡️ AI Security, Privacy & Governance
Core
Federated Learning
Federated learning trains one shared model across many devices or organizations without moving their raw data to a central server. Each participant trains locally on its own data and sends back only model updates, which a server averages into a new global model. It is the pattern an FDE reaches for when data legally or physically cannot leave its owner: hospitals, banks, and phone keyboards. The catch is that raw updates can still leak information, so real deployments layer on differential privacy or secure aggregation.
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