Efficiency Optimization Method of Wireless Federated Learning Considering Computational Capability and Channel State

Due to the explosive growth in the variety of smart mobile terminals in wireless networks, the increasing computing capability of mobile chips, and the public's growing concern for personal privacy, it is a better solution to decentralize the deep learning framework for mobile services that can...

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Bibliographic Details
Published in:Proceedings (International Conference on Communication Technology. Online) pp. 788 - 793
Main Authors: Pang, Guohao, Li, Fengguo, Zhu, Xiaorong
Format: Conference Proceeding
Language:English
Published: IEEE 20.10.2023
Subjects:
Channel allocation
Delays
Federated learning
Load modeling
parallel and distributed algorithms
Processor scheduling
resource allocation
Resource management
Schedules
scheduling policies
Task analysis
wireless communication
ISSN:2576-7828
Online Access:Get full text
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Summary:Due to the explosive growth in the variety of smart mobile terminals in wireless networks, the increasing computing capability of mobile chips, and the public's growing concern for personal privacy, it is a better solution to decentralize the deep learning framework for mobile services that can enhance user experience to the mobile terminal layer. In this paper, we study the joint optimization problem of processor performance and channel state in a non-independent distribution scenario (non-IID), while considering the user's device experience problem to improve the battery efficiency of the terminal device (TD) as much as possible and maximize the efficiency of the federated learning (FL) system while ensuring low local upload latency. To improve the efficiency of wireless federated learning (WFL), we propose a specific and complete scheduling strategy involving both computational and communication aspects. First, we model the total problem and decouple it into several sub-problems to solve according to the nature of the variables. Then, we propose a Reduced Load algorithm (RL) to solve the task allocation problem and a dynamic bandwidth allocation strategy to solve the bandwidth allocation problem. Simulation results show that the proposed scheduling strategy can achieve higher learning performance with lower training latency and is capable of adaptively adjusting the bandwidth allocation to decrease upload latency.
ISSN:2576-7828
DOI:10.1109/ICCT59356.2023.10419554