Aprendizado federado
Definição
Federated learning trains models across many devices or organizations while keeping raw data local. Only model updates (por ex. gradients) are shared, reducing privacy and regulatory risk.
Use quando data cannot be centralized (por ex. hospitals, phones) but you still want a shared machine learning model. Privacy is improved compared to sending raw data; additional techniques (differential privacy, secure aggregation) can be layered. See AI ethics for privacy and governance context.
Como funciona
O servidor mantém o modelo global e o envia aos clientes (dispositivos ou organizações). Cada cliente treina localmente em seusts own data and sends updates (gradients or model diff) back. The server aggregates updates (por ex. FedAvg: average the client models or gradients) and produces a new global model, then broadcasts again. Rounds repeat until convergence. Challenges: heterogeneity (non-IID data, different compute), communication cost (limit round count or update size), and privacy (updates can leak information; DP or secure aggregation mitigate).
Casos de uso
Federated learning fits when data must stay on devices or silos and you still want a shared model.
- Training on sensitive data (por ex. healthcare, finance) without centralizing it
- Mobile and edge devices (por ex. keyboard suggestions, on-device ML)
- Cross-organization collaboration under privacy constraints