面向能量受限工业物联网设备的联邦学习资源管理

doi:10.11959/j.issn.1000-436x.2022126

Abstract

Abstract:

Given the impact of limited wireless resources, a dynamic multi-dimensional resource joint management algorithm was proposed, which intended to tackle the problem of device failure and training interruption caused by the limited battery energy in federated learning network in industrial Internet of things (IIoT).Firstly, the optimization problem was decoupled into battery energy allocation, equipment resource allocation and communication resource allocation sub-problems which were interdependent with the goal of maximizing the fixed-time learning accuracy.Then, the equipment transmission and computing resource allocation problem were solved based on particle swarm optimization algorithm under the given energy budget.Thereafter, the resource block iterative matching algorithm was proposed to optimize the optimal communication resource allocation strategy.Finally, the online energy allocation algorithm was proposed to adjust the energy budget allocation.Simulation results validate the proposed algorithm can improve the model learning accuracy compared with other benchmarks, and can perform better in energy shortage scenarios.

Key words: federated learning, battery-powered, resource allocation, learning efficiency

CLC Number:

TP302

Shaoshuai FAN, Jianbo WU, Hui TIAN. Federated learning resource management for energy-constrained industrial IoT devices[J]. Journal on Communications, 2022, 43(8): 65-77.

Figures/Tables 8

References 44

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参数	数值
设备i处理器能源消耗系数δ_i	10^{- 28}
设备i一个样本数据的大小ε_i/bit	24.576
设备i处理每比特数据所需CPU周期数?_i/（cycle·bit^-1）	30
设备i最大发送功率P_i,max/W	0.2
噪声功率谱密度N₀ /（dBm·Hz^-1）	-174
上行资源块带宽B^Up/MHz	1
设备i最大CPU频率 f_i,max/GHz	1~2
设备i最小CPU频率 f_i,min/GHz	0.1

Federated learning resource management for energy-constrained industrial IoT devices

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