Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach

Understand

Federated learning allows mobile devices, i.e., workers, to use their local data to collaboratively train a global model required by the model owner.

• Federated learning thus addresses the privacy issues of traditional machine learning.
• However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy.
• To address the energy constraint, a power beacon can be used that recharges energy to the workers.