federated edge learning

**Federated Edge Learning** is the **application of federated learning specifically to edge devices at the network edge** — combining FL with mobile edge computing (MEC) to enable collaborative model training across edge nodes while leveraging edge computing infrastructure for efficient aggregation. **Federated Edge Architecture** - **Edge Devices**: Sensors, equipment controllers, and IoT devices perform local model training. - **Edge Server**: Local aggregation at the edge server (within the fab or site) — reduces latency and bandwidth. - **Cloud**: Optional global aggregation across sites — hierarchical FL architecture. - **Over-the-Air**: Wireless aggregation (analog over-the-air computation) for ultra-efficient communication. **Why It Matters** - **Low Latency**: Edge aggregation is faster than cloud aggregation — critical for time-sensitive applications. - **Bandwidth**: Aggregating at the edge reduces WAN bandwidth requirements. - **Semiconductor**: Edge devices in a fab can federate locally for real-time process optimization. **Federated Edge Learning** is **collaborative learning at the edge** — combining federated learning with edge computing for efficient, low-latency model training.