HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

Luo, Siqi; Chen, Xu; Wu, Qiong; Zhou, Zhi; Yu, Shuai

doi:10.1109/twc.2020.3003744

Cited by 296 publications

(175 citation statements)

References 31 publications

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“…Fig. 8 shows the convergence of the algorithm in the final model loss, total γ k i ← γ * i as defined in (19).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The selected users are allowed to determine the amount of data samples used for the model training. Two level aggregation for FL is proposed in [19] in which an intermediate model aggregation can be performed at the edge server where the final model aggregation is performed at the cloud server. The joint optimization of radio and computing resource management in MEC has been studied thoroughly in previous works.…”

Section: A Related Work 1) Resource Management In Flmentioning

confidence: 99%

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Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

et al. 2021

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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.INDEX TERMS Dataset offloading, energy-aware resource management, federated learning, generalized Nash equilibrium game, multi-access edge computing.

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“…Fig. 8 shows the convergence of the algorithm in the final model loss, total γ k i ← γ * i as defined in (19).…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: A Related Work 1) Resource Management In Flmentioning

confidence: 99%

Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

et al. 2021

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“…The proposed PerFit framework leverages edge computing to augment the computing capability of individual devices via computation offloading to mitigate the straggle effect. If we further conduct local model aggregation at the edge server, it also helps to reduce the communication overhead by avoiding massive devices to directly communicate with the cloud server over the expensive backbone network bandwidth [15]. Moreover, by performing personalization, we can deploy lightweight personalized models at some resourcelimited devices (e.g., by model pruning or transfer learning).…”

Section: Cloud-edge Framework For Personalized Federated Learningmentioning

confidence: 99%

Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework

Chen

2020

IEEE Open J. Comput. Soc.

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282

138

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Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this paper, we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneities in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications. INDEX TERMS Edge computing, federated learning, internet of things, personalization.

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“…where L is the number of local iteration, and α k /2 denotes the effective capacitance coefficient of the k-th UD's computing chipset [21].…”

Section: System Modelmentioning

confidence: 99%

Resource Allocation for Intelligent Reflecting Surfaces Assisted Federated Learning System with Imperfect CSI

Huang¹,

Han²,

Zhao

et al. 2021

Algorithms

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Due to its ability to significantly improve the wireless communication efficiency, the intelligent reflective surface (IRS) has aroused widespread research interest. However, it is a challenge to obtain perfect channel state information (CSI) for IRS-related channels due to the lack of the ability to send, receive, and process signals at IRS. Since most of the existing channel estimation methods are developed to obtain cascaded base station (BS)-IRS-user devices (UDs) channel, this paper studies the problem of computation and communication resource allocation of the IRS-assisted federated learning (FL) system based on the imperfect CSI. Specifically, we take the statistical CSI error model into consideration and formulate the training time minimization problem subject to the rate outage probability constraints. In order to solve this issue, the semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP) are invoked to transform it into a convex problem. Subsequently, a low-complexity algorithm is proposed to minimize the delay of the FL system. Numerical results show that the proposed algorithm effectively reduces the training time of the FL system base on imperfect CSI.

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HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

Cited by 296 publications

References 31 publications

Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework

Resource Allocation for Intelligent Reflecting Surfaces Assisted Federated Learning System with Imperfect CSI

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