A Novel Joint Dataset and Computation Management Scheme for Energy-Efficient Federated Learning in Mobile Edge Computing

Kim, Jingyeom; Kim, Doyeon; Lee, Joohyung; Hwang, Jenn Jiang

doi:10.1109/lwc.2022.3147236

Cited by 23 publications

(21 citation statements)

References 16 publications

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“…In particular, [15] proposes an FL algorithm that adapts the compression parameters to minimize energy consumption at UEs. The work of [16] proposes a novel joint dataset and computation management scheme that trades off between learning accuracy and energy consumption for energy-efficient FL in mobile edge computing. Reference [17] introduces a federated meta-learning algorithm together with a resource allocation scheme to jointly improve convergence rate and minimize energy cost.…”

Section: A Review Of Related Literaturementioning

confidence: 99%

Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

Ngo

Dao

et al. 2022

IEEE Open J. Commun. Soc.

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This work proposes novel synchronous, asynchronous, and session-based designs for energy-efficient massive multiple-input multiple-output networks to support federated learning (FL). The synchronous design relies on strict synchronization among users when executing each FL communication round, while the asynchronous design allows more flexibility for users to save energy by using lower computing frequencies. The session-based design splits the downlink and uplink phases in each FL communication round into separate sessions. In this design, we assign users such that one of the participating users in each session finishes its transmission and does not join the next session. As such, more power and degrees of freedom will be allocated to unfinished users, resulting in higher rates, lower transmission times, and hence, higher energy efficiency. In all three designs, we use zero-forcing processing for both uplink and downlink, and develop algorithms that optimize user assignment, time allocation, power, and computing frequencies to minimize the energy consumption at the base station and users, while guaranteeing a predefined maximum execution time of each FL communication round.

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Section: A Review Of Related Literaturementioning

confidence: 99%

Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

Ngo

Dao

et al. 2022

IEEE Open J. Commun. Soc.

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“…Specifically, FL suffers from long transmission latency to a remote cloud server [ 4 ]. Additionally, the varying qualities of each MD’s dataset can impact the accuracy of the global model, and the local training burden may make MDs reluctant to participate in the FL process [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…In terms of radio and computation resource management, given the constraints of limited communication bandwidth and computational capacity, various approaches to joint edge association and the allocation of radio and computation resources have been explored. These endeavors aim to enhance the accuracy of the global model or improve the speed of convergence as well as energy efficiency [ 6 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Ren et al [ 9 ] proposed a joint batch size selection and communication resource allocation algorithm for both CPU and GPU resources, taking into account the trade-off between loss decay and latency. Additionally, Kim et al [ 6 ] proposed an energy-efficient joint dataset and computation management scheme that utilizes dataset management to reduce the burden on MDs, enabling energy-efficient FL with minimal accuracy loss. However, most of these works assume that the value of the dataset remains constant throughout the FL process.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Dataset Management Scheme for Lightweight Federated Learning in Mobile Edge Computing

Kim,

Bang,

Lee

2024

Sensors

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Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback–Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy.

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“…Therefore, the computing resource for FL has received extensive attention. Zhang et al [11] considered a novel joint dataset and computing resource allocation scheme for FL systems and established an energy consumption minimization problem under the requirement of the finishing training time. Huang et al [12] proposed a model training convergence minimization problem under the energy consumption constraint, in which the CPU frequency and the phase shifts are optimized jointly.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Bandwidth-Limited Federated Learning in Wireless Edge Systems

Zhou

Liu

Lei

2023

IEEE Open J. Commun. Soc.

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This paper studies a bandwidth-limited federated learning (FL) system where the access point is a central server for aggregation and the energy-constrained user equipemnts (UEs) with limited computation capabilities (e.g., Internet of Things devices) perform local training. Limited by the bandwidth in wireless edge systems, only a part of UEs can participate in each FL training round. Selecting different UEs could affect the FL performance, and selected UEs need to allocate their computing resource effectively. In wireless edge FL systems, simultaneously accelerating FL training and reducing computingcommunication energy consumption are of importance. To this end, we formulate a multi-objective optimization problem (MOP). In MOP, the model training convergence is difficult to calculate accurately. Meanwhile, MOP is a combinatorial optimization problem, with the high-dimension mix-integer variables, which is proved to be NP-hard. To address these challenges, a multi-objective evolutionary algorithm for the bandwidth-limited FL system (MOEA-FL) is proposed to obtain a Pareto optimal solution set. In MOEA-FL, an age-of-update-loss method is first proposed to transform the original global loss function into a convergence reference function. Then, MOEA-FL divides MOP into N single objective subproblems by the Tchebycheff approach and optimizes the subproblems simultaneously by evolving a population. Extensive experiments have been carried out on MNIST dataset and a medical case called TissueMNIST dataset for both the i.i.d and non-i.i.d data setting. Experimental results demonstrate that MOEA-FL performs better than other algorithms and verify the robustness and scalability of MOEA-FL.

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A Novel Joint Dataset and Computation Management Scheme for Energy-Efficient Federated Learning in Mobile Edge Computing

Cited by 23 publications

References 16 publications

Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

Adaptive Dataset Management Scheme for Lightweight Federated Learning in Mobile Edge Computing

Multi-Objective Optimization for Bandwidth-Limited Federated Learning in Wireless Edge Systems

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