Federated learning addresses the fundamental problems of centralized AI such as privacy, ownership, and locality of data. It extends, even disrupts, the centralized AI paradigm in which better algorithms always comes at the cost of collecting more and more sensitive data.
Federated learning enables:
- Data security and privacy where data never moves
- Reduced communication complexity and costs
- Powerful data network effects in industries where data cannot be transferred