# Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/1812.03633 · HTML: https://mlsanity.com/paper/1812.03633/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT](https://mlsanity.com/paper/2202.03575/) (2022)
- [Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT](https://mlsanity.com/paper/2202.07391/) (2022)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [Scheduling Algorithms for Federated Learning with Minimal Energy Consumption](https://mlsanity.com/paper/2209.06210/) (2022)
- [Federated Learning for Energy Constrained IoT devices: A systematic mapping study](https://mlsanity.com/paper/2301.03720/) (2023)
- [The Two Faces of AI in Green Mobile Computing: A Literature Review](https://mlsanity.com/paper/2308.04436/) (2023)

## Abstract (arXiv)

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.