  In this letter, we consider the concept of Mobile Crowd-Machine Learning
(MCML) for a federated learning model. The MCML enables mobile devices in a
mobile network to collaboratively train neural network models required by a
server while keeping data on the mobile devices. The MCML thus addresses data
privacy issues of traditional machine learning. However, the mobile devices are
constrained by energy, CPU, and wireless bandwidth. Thus, to minimize the
energy consumption, training time and communication cost, the server needs to
determine proper amounts of data and energy that the mobile devices use for
training. However, under the dynamics and uncertainty of the mobile
environment, it is challenging for the server to determine the optimal
decisions on mobile device resource management. In this letter, we propose to
adopt a deep- Q learning algorithm that allows the server to learn and find
optimal decisions without any a priori knowledge of network dynamics.
Simulation results show that the proposed algorithm outperforms the static
algorithms in terms of energy consumption and training latency.
