federated learning

Examines AI’s shift from cloud to on-device intelligence, enabling privacy, low latency, and offline reliability. Covers advances in compact models, edge chips, and federated learning; trade-offs in power, memory, and accuracy; and use cases across phones, wearables, homes, and cars. Stresses responsible design, open tooling, and policy as keys to a future where personalized, secure AI runs locally for everyone.