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:10.48550/arxiv.2002.11343

Cited by 6 publications

(16 citation statements)

References 17 publications

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“…The package loss rate 𝛾 is given as a background network parameter. Then from the definitions of 𝐶 𝑢 and 𝐶 𝑛 in (8) and (11), the joint optimization problem for FL implementation over a wireless network can be expressed as follows.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Due to different conditions of the clients, the specific system cost in each global epoch may be varied. Thus, in the definition of 𝐶 𝑢 and 𝐶 𝑛 in ( 8) and (11), the expectation on 𝑃 𝑡 is taken to get the expected cost for each global epoch.…”

Section: A Sub-problems For Hyper-parameters and Scheduling Policymentioning

confidence: 99%

“…From the point of FL implementation, some works discussed the FL scheme and the scheduling algorithm therein [6,[8][9][10][11][12][13]. Authors of [13] proposed a multi-player game to study participants' reactions under various incentive mechanisms in FL scenarios.…”

mentioning

confidence: 99%

“…data set and partial participation were not considered, which restricts its generality. [11,12] also considered the optimization of communication settings. Although their communication models are solid, the convergence analysis still needs to be improved.…”

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confidence: 99%

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Convergence Analysis and System Design for Federated Learning over Wireless Networks

Wan¹,

Lu²,

Fan³

et al. 2021

Preprint

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Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. However, as the training data in FL is not collected and stored centrally, FL training requires frequent model exchange, which is largely affected by the wireless communication network. Therein, limited bandwidth and random package loss restrict interactions in training. Meanwhile, the insufficient message synchronization among distributed clients could also affect FL convergence. In this paper, we analyze the convergence rate of FL training considering the joint impact of communication network and training settings. Further by considering the training costs in terms of time and power, the optimal scheduling problems for communication networks are formulated. The developed theoretical results can be used to assist the system parameter selections and explain the principle of how the wireless communication system could influence the distributed training process and network scheduling.

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Section: Problem Formulationmentioning

confidence: 99%

Section: A Sub-problems For Hyper-parameters and Scheduling Policymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Convergence Analysis and System Design for Federated Learning over Wireless Networks

Wan¹,

Lu²,

Fan³

et al. 2021

Preprint

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“…Literature in FL cost optimization mainly focused on learning time and on-device energy consumption in mobile edge networks. The optimization of learning time was studied in [8], [27]- [33], and joint optimization for learning time and energy consumption was considered in [34]- [37]. These works considered resource (e.g., transmission power, communication bandwidth, and CPU frequency) allocation (e.g., [27], [28], [34]- [37]), cost-aware client selection (e.g., [29], [30]), client scheduling (e.g., [31]- [33]), and model pruning [8] for prespecified (i.e., non-optimized) design parameters (K and E in our case) of the FL algorithm.…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Federated Learning in Mobile Edge Networks

Luo¹,

Li²,

Wang³

et al. 2021

Preprint

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Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings.

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

Wu¹,

He²,

Chen³

2020

Preprint

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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 article 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.

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

Cited by 6 publications

References 17 publications

Convergence Analysis and System Design for Federated Learning over Wireless Networks

Convergence Analysis and System Design for Federated Learning over Wireless Networks

Cost-Effective Federated Learning in Mobile Edge Networks

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

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