TY - JOUR
T1 - Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems
AU - Wutyee Zaw, Chit
AU - Pandey, Shashi Raj
AU - Kim, Kitae
AU - Hong, Choong Seon
N1 - Funding Information:
This work was supported in part by the Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (MSIT), Evolvable Deep Learning Model Generation Platform for Edge Computing, under Grant 2019-0-01287, and in part by the Ministry of Science and ICT (MSIT), South Korea, through the Grand Information Technology Research Center Support Program, supervised by the Institute for Information and communications Technology Planning and Evaluation (IITP), under Grant IITP-2020-2015-0-00742.
Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always at stake to meet the objective of local energy minimization. In this regard, Multi-access Edge Computing (MEC)-enabled FL addresses the tradeoff between the model performance and the energy consumption of the mobile devices by allowing users to offload a portion of their local dataset to an edge server for the model training. Since the edge server has high computation capability, the time consumption of the model training at the edge server is insignificant. However, the time consumption for dataset offloading from mobile users to the edge server has a significant impact on the total time consumed to complete a single round of FL process. Thus, resource management in MEC-enabled FL is challenging, where the objective is to reduce the total time consumption while saving the energy consumption of the mobile devices. In this article, we formulate an energy-aware resource management for MEC-enabled FL in which the model training loss and the total time consumption are jointly minimized, while considering the energy limitation of mobile devices. In addition, we recast the formulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capture the coupling constraints between the radio resource management and dataset offloading. To that end, we analyze the impact of the dataset offloading and computing resource allocation on the model training loss, time, and the energy consumption. Finally, we present the convergence analysis of the proposed solution, and evaluate its performance against the traditional FL approach. Simulation results demonstrate the efficacy of our proposed solution approach.
AB - In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always at stake to meet the objective of local energy minimization. In this regard, Multi-access Edge Computing (MEC)-enabled FL addresses the tradeoff between the model performance and the energy consumption of the mobile devices by allowing users to offload a portion of their local dataset to an edge server for the model training. Since the edge server has high computation capability, the time consumption of the model training at the edge server is insignificant. However, the time consumption for dataset offloading from mobile users to the edge server has a significant impact on the total time consumed to complete a single round of FL process. Thus, resource management in MEC-enabled FL is challenging, where the objective is to reduce the total time consumption while saving the energy consumption of the mobile devices. In this article, we formulate an energy-aware resource management for MEC-enabled FL in which the model training loss and the total time consumption are jointly minimized, while considering the energy limitation of mobile devices. In addition, we recast the formulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capture the coupling constraints between the radio resource management and dataset offloading. To that end, we analyze the impact of the dataset offloading and computing resource allocation on the model training loss, time, and the energy consumption. Finally, we present the convergence analysis of the proposed solution, and evaluate its performance against the traditional FL approach. Simulation results demonstrate the efficacy of our proposed solution approach.
KW - Dataset offloading
KW - energy-aware resource management
KW - federated learning
KW - generalized Nash equilibrium game
KW - multi-access edge computing
UR - http://www.scopus.com/inward/record.url?scp=85100516144&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3055523
DO - 10.1109/ACCESS.2021.3055523
M3 - Journal article
AN - SCOPUS:85100516144
SN - 2169-3536
VL - 9
SP - 34938
EP - 34950
JO - IEEE Access
JF - IEEE Access
M1 - 9340296
ER -