To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices Article Swipe

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Pavana Prakash , Jiahao Ding , Maoqiang Wu , Minglei Shu , Rong Yu , Miao Pan ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2111.00637

Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In FL, since mobile devices collaborate to train a model based on their own data under the coordination of a central server by sharing just the model updates, training data is maintained private. However, without the central availability of data, computing nodes need to communicate the model updates often to attain convergence. Hence, the local computation time to create local model updates along with the time taken for transmitting them to and from the server result in a delay in the overall time. Furthermore, unreliable network connections may obstruct an efficient communication of these updates. To address these, in this paper, we propose a delay-efficient FL mechanism that reduces the overall time (consisting of both the computation and communication latencies) and communication rounds required for the model to converge. Exploring the impact of various parameters contributing to delay, we seek to balance the trade-off between wireless communication (to talk) and local computation (to work). We formulate a relation with overall time as an optimization problem and demonstrate the efficacy of our approach through extensive simulations.

Related Topics

Computer Science

Enhanced Data Rates For Gsm Evolution

Edge Computing

Mobile Device

Wireless

Artificial Intelligence

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Computer science Computation Edge device Distributed computing Enhanced Data Rates for GSM Evolution Edge computing Mobile device Wireless Convergence (economics) Models of communication Computer network Mobile edge computing Artificial intelligence Cloud computing Telecommunications World Wide Web Operating system Economic growth Algorithm Economics Communication Sociology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2111.00637
PDF: https://arxiv.org/pdf/2111.00637
OA Status: green
References: 9
Related Works: 10
OpenAlex ID: https://openalex.org/W3210350562

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DOI: https://doi.org/10.48550/arxiv.2111.00637

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Title: To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-11-01

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Authors: Pavana Prakash, Jiahao Ding, Maoqiang Wu, Minglei Shu, Rong Yu, Miao Pan

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Landing page: https://arxiv.org/abs/2111.00637

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Concepts: Computer science, Computation, Edge device, Distributed computing, Enhanced Data Rates for GSM Evolution, Edge computing, Mobile device, Wireless, Convergence (economics), Models of communication, Computer network, Mobile edge computing, Artificial intelligence, Cloud computing, Telecommunications, World Wide Web, Operating system, Economic growth, Algorithm, Economics, Communication, Sociology

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abstract_inverted_index.However,	58
abstract_inverted_index.approach	196
abstract_inverted_index.devices.	24
abstract_inverted_index.efficacy	193
abstract_inverted_index.emerging	4
abstract_inverted_index.learning	1, 7
abstract_inverted_index.obstruct	113
abstract_inverted_index.private.	57
abstract_inverted_index.relation	182
abstract_inverted_index.required	148
abstract_inverted_index.training	53
abstract_inverted_index.updates,	52
abstract_inverted_index.updates.	119
abstract_inverted_index.wireless	170
abstract_inverted_index.Exploring	154
abstract_inverted_index.Federated	0
abstract_inverted_index.computing	13, 65
abstract_inverted_index.converge.	153
abstract_inverted_index.efficient	115
abstract_inverted_index.extensive	198
abstract_inverted_index.formulate	180
abstract_inverted_index.mechanism	131
abstract_inverted_index.paradigm,	8
abstract_inverted_index.promising	16
abstract_inverted_index.trade-off	168
abstract_inverted_index.latencies)	144
abstract_inverted_index.maintained	56
abstract_inverted_index.parameters	159
abstract_inverted_index.unreliable	109
abstract_inverted_index.(consisting	137
abstract_inverted_index.collaborate	30
abstract_inverted_index.communicate	69
abstract_inverted_index.computation	80, 141, 176
abstract_inverted_index.connections	111
abstract_inverted_index.demonstrate	191
abstract_inverted_index.distributed	5
abstract_inverted_index.Furthermore,	108
abstract_inverted_index.applications	20
abstract_inverted_index.availability	62
abstract_inverted_index.contributing	160
abstract_inverted_index.convergence.	76
abstract_inverted_index.coordination	42
abstract_inverted_index.optimization	188
abstract_inverted_index.simulations.	199
abstract_inverted_index.transmitting	93
abstract_inverted_index.communication	116, 143, 146, 171
abstract_inverted_index.delay-efficient	129
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	6
citation_normalized_percentile