cross-device federated learning

**Cross-Device Federated Learning** is a **federated learning setting involving millions of edge devices (smartphones, IoT sensors, equipment controllers)** — each device has a tiny local dataset, limited compute, unreliable connectivity, and only a fraction participate in each training round. **Cross-Device Characteristics** - **Many Participants**: Millions to billions of devices (smartphones, sensors, controllers). - **Unreliable**: Devices go offline, have intermittent connectivity, and varying compute capabilities. - **Tiny Local Data**: Each device has very little local data — model must learn from many partial views. - **Asynchronous**: No guarantee all selected devices complete their update within the time window. **Why It Matters** - **Scale**: Google trains keyboard prediction models on billions of phones using cross-device FL. - **Privacy at Scale**: Each user's data stays on their device — no central data collection. - **Semiconductor IoT**: Edge sensors in fabs could use cross-device FL for distributed monitoring models. **Cross-Device FL** is **learning from the edge swarm** — training on millions of unreliable, resource-constrained devices for privacy-preserving intelligence at scale.