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dc.contributor.authorChen, Zen_US
dc.contributor.authorYi, Wen_US
dc.contributor.authorShin, Hen_US
dc.contributor.authorNallanathan, Aen_US
dc.date.accessioned2024-07-15T08:51:41Z
dc.date.issued2024-01-01en_US
dc.identifier.issn1536-1276en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/98110
dc.description.abstractMost existing wireless federated learning (FL) studies focused on homogeneous model settings where devices train identical local models. In this setting, the devices with poor communication and computation capabilities may delay the global model update and degrade the performance of FL. Moreover, in the homogenous model settings, the scale of the global model is restricted by the device with the lowest capability. To tackle these challenges, this work proposes an adaptive model pruning-based FL (AMP-FL) framework, where the edge server dynamically generates sub-models by pruning the global model for devices' local training to adapt their heterogeneous computation capabilities and time-varying channel conditions. Since the involvement of diverse structures of devices' sub-models in the global model updating may negatively affect the training convergence, we propose compensating for the gradients of pruned model regions by devices' historical gradients. We then introduce an age of information (AoI) metric to characterize the staleness of local gradients and theoretically analyze the convergence behaviour of AMP-FL. The convergence bound suggests scheduling devices with large AoI of gradients and pruning the model regions with small AoI for devices to improve the learning performance. Inspired by this, we define a new objective function, i.e., the average AoI of local gradients, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, model pruning, and resource block (RB) allocation design. Through detailed analysis, we derive the optimal model pruning strategy and transform the RB allocation problem into equivalent linear programming that can be effectively solved. Experimental results demonstrate the effectiveness and superiority of the proposed approaches. The proposed AMP-FL is capable of achieving 1.9x and 1.6x speed up for FL on MNIST and CIFAR-10 datasets in comparison with the FL schemes with homogeneous model settings.en_US
dc.format.extent7582 - 7598en_US
dc.relation.ispartofIEEE Transactions on Wireless Communicationsen_US
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleAdaptive Model Pruning for Communication and Computation Efficient Wireless Federated Learningen_US
dc.typeArticle
dc.identifier.doi10.1109/TWC.2023.3342626en_US
pubs.issue7en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume23en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.funder.projectb215eee3-195d-4c4f-a85d-169a4331c138en_US


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