Joint Resource Allocation for Efficient Federated Learning in Internet of Things Supported by Edge Computing

Ren, Jianyang; Sun, Junshuai; Tian, Hui; Ni, Wanli; Nie, Gaofeng; Wang, Yingying

doi:10.1109/iccworkshops50388.2021.9473734

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…To develop resource allocation mechanisms to facilitate energyefficient FL in an edge intelligence and improve the number of connected devices. The authors in [19][20][21][22] focused on addressing the training of the local models to provide more energy efficiency and reduce computation time and wireless data on IoT devices with minimal CPU frequency allocation. For a sustainable operation of an FL framework, it is imperative to reduce energy consumption and satisfy the FL time of edge intelligence nodes.…”

Section: A Related Workmentioning

confidence: 99%

Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G

et al. 2023

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An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices. The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections. Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices. The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form. The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes. The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time.INDEX TERMS Internet-of-things, federated learning, energy consumption, edge nodes, central processing unit.

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Section: A Related Workmentioning

confidence: 99%

Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G

et al. 2023

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“…In essence, to efficiently deploy FL over real-life cellular networks, it is essential to examine how the wireless factors impact the system performance of FL procedures. In [41][42], the authors described an analytical pattern of the influence of symbol transmission faults on the FL performance and, hence, did not consider optimal client selection, power allocation, and wireless resource distribution, interference management to optimize the network performance. Furthermore, a holistic discussion of the integrated FL-based smart services and applications are still absent [38].…”

Section: Fl and Marl-based Fl Frameworkmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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“…Recently, many existing works have focused on communication efficient FL [1]- [6]. To reduce both FL latency and energy consumption, Dinh et.…”

Section: Introductionmentioning

confidence: 99%

“…Considering an edge computing-based FL system, Ren et. al in [6] minimized the weighted sum of communication and learning cost. However, in the above literature, the strong convexity assumption limits the applications of their schemes in DNN or other models with non-convex loss function.…”

Section: Introductionmentioning

confidence: 99%

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Towards Communication-Learning Trade-off for Federated Learning at the Network Edge

Ren,

Ni,

Tian

2022

Preprint

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In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learning performance due to the information loss. Thus, a trade-off problem between communication and learning is raised. To address this challenge, we quantify the effects of network pruning and packet error on the learning performance by deriving the convergence rate of FL with a non-convex loss function. Then, closed-form solutions for pruning control and bandwidth allocation are proposed to minimize the weighted sum of FL latency and FL performance. Finally, numerical results demonstrate that i) our proposed solution can outperform benchmarks in terms of cost reduction and accuracy guarantee, and ii) a higher pruning rate would bring less communication overhead but also worsen FL accuracy, which is consistent with our theoretical analysis.

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Joint Resource Allocation for Efficient Federated Learning in Internet of Things Supported by Edge Computing

Cited by 13 publications

References 13 publications

Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G

Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Towards Communication-Learning Trade-off for Federated Learning at the Network Edge

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