Device Scheduling with Fast Convergence for Wireless Federated Learning

Understand

Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training framework called federated learning (FL) has emerged.

• In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels.
• Due to the limited spectrum resources, only a portion of the devices can be scheduled in each round.
• While most of the existing work on scheduling focuses on the convergence of FL w.r.t.