TY - JOUR
T1 - Federated Learning Over Wireless IoT Networks With Optimized Communication and Resources
AU - Chen, Hao
AU - Huang, Shaocheng
AU - Zhang, Deyou
AU - Xiao, Ming
AU - Skoglund, Mikael
AU - Poor, H. Vincent
N1 - Funding Information:
This work was supported in part by the ERANET Smart Energy Systems SG+ 2017 Program through "SMART-MLA" Project under Grant 89029 (and SWEA number 42811-2), the FORMAS Project titled "Intelligent Energy Management in Smart Community with Distributed Machine Learning" under Grant 2021-00306, and the Swedish Research Council Project titled "Coding for Large-Scale Distributed Machine Learning" under Grant 2021-04772. The work of Deyou Zhang was supported by the Digital Futures Postdoc Fellowships.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique, especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of Things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two subproblems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables, including power variables, bandwidth variables, and transmission indicators. Then, a linear-search-based power and bandwidth allocation method is developed. Given appropriate hyperparameters, we show that the proposed communication-efficient FL (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.
AB - To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique, especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of Things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two subproblems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables, including power variables, bandwidth variables, and transmission indicators. Then, a linear-search-based power and bandwidth allocation method is developed. Given appropriate hyperparameters, we show that the proposed communication-efficient FL (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.
KW - Communication efficiency
KW - federated learning (FL)
KW - resource allocation
KW - wireless Internet of Things (IoT) networks
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U2 - 10.1109/JIOT.2022.3151193
DO - 10.1109/JIOT.2022.3151193
M3 - Article
AN - SCOPUS:85124846600
VL - 9
SP - 16592
EP - 16605
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
IS - 17
ER -