Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

Understand

We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL).

• Unlike existing resource allocation methods for FL, resource rationing focuses on balancing resources across learning rounds so that their collective impact on the federated learning performance is explicitly captured.
• This new framework can be integrated seamlessly with existing resource allocation schemes to optimize the convergence of FL.
• In particular, a novel "later-is-better" principle is at the front and center of resource rationing, which is validated empirically in several instances of wireless FL.