A mobility-aware federated learning coordination algorithm

Júnior, Daniel Paiva de Macêdo; Santos, Danilo F. S.; Perkusich, Angelo; Valadares, Dalton Cézane Gomes

doi:10.1007/s11227-023-05372-3

Cited by 1 publication

(1 citation statement)

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“…However, each central server independently solved an optimization problem to select clients, resulting in a solution not coordinated with other central servers. We observed that solving the optimization problem for client selection required significant computational resources, which could pose challenges for central servers with limited computational capabilities [ 25 ]. In the current version of MoFeL , we consider another profile for the FL coordination, named the central station.…”

Section: Introductionmentioning

confidence: 99%

Mobility-Aware Federated Learning Considering Multiple Networks

Macedo

Santos

Perkusich

et al. 2023

Sensors

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Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics. For instance, mobility can hinder training by causing a client dropout when a device loses connection with other devices on the network. To address this issue, we propose a FL coordination algorithm, MoFeL, to ensure efficient training even in scenarios with mobility. Furthermore, MoFeL evaluates multiple networks with different central servers. To evaluate its effectiveness, we conducted simulation experiments using an image classification application that utilizes machine models trained by a convolutional neural network. The simulation results demonstrate that MoFeL outperforms traditional training coordination algorithms in FL, with 156.5% more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects.

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Section: Introductionmentioning

confidence: 99%