BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices

Ibraimi, Lenart; Selimi, Mennan; Freitag, Félix

doi:10.1109/globecom46510.2021.9685095

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The communication cost is incurred due to the downloading of the global model from service S m to each UE and uploading the trained local models of UEs. Following existing studies [41], [15], [56], we assume that the communication cost is mainly due to the consumption of bandwidth resources, which is proportional to the amount of data to be transferred. Let c t k,q,i be the cost of transmitting a unit amount of data from UE ue k to location Loc q via base station bs i .…”

Section: Cost Modelsmentioning

confidence: 99%

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HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

Zhao

Liang

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

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Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues -especially when multiple users issue concurrent FL requests. Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimisation associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.

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Section: Cost Modelsmentioning

confidence: 99%

“…Following the similar derivation in Ineq. (15), the probability of violating the computing resource capacity on each Loc…”

Section: Proofmentioning

confidence: 99%

HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

Zhao

Liang

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

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“…This approach uses weight-sharing methods for searching neural architectures. The training process for FL must not only be aimed at high accuracy but also at reducing the training time and resource consumption in practical environments, using low-capacity computing devices [37,38]. FL uses best epoch algorithm to determine how many epochs are necessary per training round.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Genetic Clustered Federated Learning for COVID-19 Detection

Kandati

Gadekallu

2022

Electronics

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Coronavirus (COVID-19) has caused a global disaster with adverse effects on global health and the economy. Early detection of COVID-19 symptoms will help to reduce the severity of the disease. As a result, establishing a method for the initial recognition of COVID-19 is much needed. Artificial Intelligence (AI) plays a vital role in detection of COVID-19 cases. In the process of COVID-19 detection, AI requires access to patient personal records which are sensitive. The data shared can pose a threat to the privacy of patients. This necessitates a technique that can accurately detect the COVID-19 patients in a privacy preserving manner. Federated Learning (FL) is a promising solution, which can detect the COVID-19 disease at early stages without compromising the sensitive information of the patients. In this paper, we propose a novel hybrid algorithm named genetic clustered FL (Genetic CFL), that groups edge devices based on the hypertuned parameters and modifies the parameters cluster wise genetically. The experimental results proved that the proposed Genetic CFL approach performed better than conventional AI approaches.

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Performance Evaluation of Federated Learning Over Wireless Mesh Networks with Low-Capacity Devices

Freitag

Vilchez

Wei

et al. 2022

Lecture Notes in Networks and Systems

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Federated learning is a distributed learning technique in which a machine learning model is trained collaboratively among several nodes. While the privacy preservation of the training data is one of the important promises of federated learning, there is also an opportunity to use low capacity devices for machine learning model training by taking advantage of the fact that the training effort is divided among many nodes. In this paper we conduct experiments with a federated learning network deployed on several low capacity devices connected to a wireless mesh network. The measurements show the hardware capacity and link bandwidth of the clients on the federated learning process. The results suggest that for heterogeneous networks the federated learning clients should be extended with more autonomous decision capacities according to the network and local conditions.

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BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices

Cited by 6 publications

References 12 publications

HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

Genetic Clustered Federated Learning for COVID-19 Detection

Performance Evaluation of Federated Learning Over Wireless Mesh Networks with Low-Capacity Devices

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