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

By Tran The Anh, Nguyen Cong Luong, Dusist Niyato, Dong In Kim, Li-Chun Wang. 2018

82 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Science Applications

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Citations in the corpus

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach (2019, methodology): “Therefore, it is essential for the task publishers to design an efficient incentive mechanism to reduce the impact of information asymmetry [1], [6].”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, methodology): “The resource limitation problem on client devices in terms of CPU, energy and bandwidth has been considered in [122].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “Also in [116], a DRL algorithm was devised for efficient training management which exhibited superiority”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, unknown): “[71] propose a deep Q-learning algorithm for”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “resource allocation for model training as proposed in [119].”
Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory (2019, methodology): “[8] utilized a deep Q learning algorithm to solve the optimal data and energy management problems of federated learning without prior knowledge of network dynamics.”
Federated Learning for Healthcare Informatics (2019, methodology): “Anh [5] thus proposes to use the Deep Q-Learning [102] technique that enables the server to find the optimal data and energy management for the mobile devices participating in the mobile crowdmachine learning through federated learning without any prior knowledge of network dynamics.”