Efficient training management for mobile crowd-machine learning: A deep reinforcement learning approach
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 …
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 …
Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach
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 …
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 …
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