Efficient Model Compression for Hierarchical Federated Learning Article Swipe

PDF

Xi Zhu , Songcan Yu , Junbo Wang , Qinglin Yang ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2405.17522

Federated learning (FL), as an emerging collaborative learning paradigm, has garnered significant attention due to its capacity to preserve privacy within distributed learning systems. In these systems, clients collaboratively train a unified neural network model using their local datasets and share model parameters rather than raw data, enhancing privacy. Predominantly, FL systems are designed for mobile and edge computing environments where training typically occurs over wireless networks. Consequently, as model sizes increase, the conventional FL frameworks increasingly consume substantial communication resources. To address this challenge and improve communication efficiency, this paper introduces a novel hierarchical FL framework that integrates the benefits of clustered FL and model compression. We present an adaptive clustering algorithm that identifies a core client and dynamically organizes clients into clusters. Furthermore, to enhance transmission efficiency, each core client implements a local aggregation with compression (LC aggregation) algorithm after collecting compressed models from other clients within the same cluster. Simulation results affirm that our proposed algorithms not only maintain comparable predictive accuracy but also significantly reduce energy consumption relative to existing FL mechanisms.

Related Topics

Computer Science

Artificial Intelligence

Composite Material

Materials Science

Concepts

Computer science Compression (physics) Artificial intelligence Composite material Materials science

Metadata

Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2405.17522
PDF: https://arxiv.org/pdf/2405.17522
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4399151259

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4399151259

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.48550/arxiv.2405.17522

Digital Object Identifier
Title: Efficient Model Compression for Hierarchical Federated Learning

Work title
Type: preprint

OpenAlex work type
Language: en

Primary language
Publication year: 2024

Year of publication
Publication date: 2024-05-27

Full publication date if available
Authors: Xi Zhu, Songcan Yu, Junbo Wang, Qinglin Yang

List of authors in order
Landing page: https://arxiv.org/abs/2405.17522

Publisher landing page
PDF URL: https://arxiv.org/pdf/2405.17522

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://arxiv.org/pdf/2405.17522

Direct OA link when available
Concepts: Computer science, Compression (physics), Artificial intelligence, Composite material, Materials science

Top concepts (fields/topics) attached by OpenAlex
Cited by: 0

Total citation count in OpenAlex
Related works (count): 10

Other works algorithmically related by OpenAlex

Full payload

id	https://openalex.org/W4399151259
doi	https://doi.org/10.48550/arxiv.2405.17522
ids.doi	https://doi.org/10.48550/arxiv.2405.17522
ids.openalex	https://openalex.org/W4399151259
fwci
type	preprint
title	Efficient Model Compression for Hierarchical Federated Learning
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
topics[0].id	https://openalex.org/T10764
topics[0].field.id	https://openalex.org/fields/17
topics[0].field.display_name	Computer Science
topics[0].score	0.9900000095367432
topics[0].domain.id	https://openalex.org/domains/3
topics[0].domain.display_name	Physical Sciences
topics[0].subfield.id	https://openalex.org/subfields/1702
topics[0].subfield.display_name	Artificial Intelligence
topics[0].display_name	Privacy-Preserving Technologies in Data
topics[1].id	https://openalex.org/T10203
topics[1].field.id	https://openalex.org/fields/17
topics[1].field.display_name	Computer Science
topics[1].score	0.9609000086784363
topics[1].domain.id	https://openalex.org/domains/3
topics[1].domain.display_name	Physical Sciences
topics[1].subfield.id	https://openalex.org/subfields/1710
topics[1].subfield.display_name	Information Systems
topics[1].display_name	Recommender Systems and Techniques
topics[2].id	https://openalex.org/T11273
topics[2].field.id	https://openalex.org/fields/17
topics[2].field.display_name	Computer Science
topics[2].score	0.9462000131607056
topics[2].domain.id	https://openalex.org/domains/3
topics[2].domain.display_name	Physical Sciences
topics[2].subfield.id	https://openalex.org/subfields/1702
topics[2].subfield.display_name	Artificial Intelligence
topics[2].display_name	Advanced Graph Neural Networks
is_xpac	False
apc_list
apc_paid
concepts[0].id	https://openalex.org/C41008148
concepts[0].level	0
concepts[0].score	0.6311397552490234
concepts[0].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[0].display_name	Computer science
concepts[1].id	https://openalex.org/C180016635
concepts[1].level	2
concepts[1].score	0.4637857675552368
concepts[1].wikidata	https://www.wikidata.org/wiki/Q2712821
concepts[1].display_name	Compression (physics)
concepts[2].id	https://openalex.org/C154945302
concepts[2].level	1
concepts[2].score	0.36368417739868164
concepts[2].wikidata	https://www.wikidata.org/wiki/Q11660
concepts[2].display_name	Artificial intelligence
concepts[3].id	https://openalex.org/C159985019
concepts[3].level	1
concepts[3].score	0.0
concepts[3].wikidata	https://www.wikidata.org/wiki/Q181790
concepts[3].display_name	Composite material
concepts[4].id	https://openalex.org/C192562407
concepts[4].level	0
concepts[4].score	0.0
concepts[4].wikidata	https://www.wikidata.org/wiki/Q228736
concepts[4].display_name	Materials science
keywords[0].id	https://openalex.org/keywords/computer-science
keywords[0].score	0.6311397552490234
keywords[0].display_name	Computer science
keywords[1].id	https://openalex.org/keywords/compression
keywords[1].score	0.4637857675552368
keywords[1].display_name	Compression (physics)
keywords[2].id	https://openalex.org/keywords/artificial-intelligence
keywords[2].score	0.36368417739868164
keywords[2].display_name	Artificial intelligence
language	en
locations[0].id	pmh:oai:arXiv.org:2405.17522
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S4306400194
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	arXiv (Cornell University)
locations[0].source.host_organization	https://openalex.org/I205783295
locations[0].source.host_organization_name	Cornell University
locations[0].source.host_organization_lineage	https://openalex.org/I205783295
locations[0].license
locations[0].pdf_url	https://arxiv.org/pdf/2405.17522
locations[0].version	submittedVersion
locations[0].raw_type	text
locations[0].license_id
locations[0].is_accepted	False
locations[0].is_published	False
locations[0].raw_source_name
locations[0].landing_page_url	http://arxiv.org/abs/2405.17522
locations[1].id	doi:10.48550/arxiv.2405.17522
locations[1].is_oa	True
locations[1].source.id	https://openalex.org/S4306400194
locations[1].source.issn
locations[1].source.type	repository
locations[1].source.is_oa	True
locations[1].source.issn_l
locations[1].source.is_core	False
locations[1].source.is_in_doaj	False
locations[1].source.display_name	arXiv (Cornell University)
locations[1].source.host_organization	https://openalex.org/I205783295
locations[1].source.host_organization_name	Cornell University
locations[1].source.host_organization_lineage	https://openalex.org/I205783295
locations[1].license
locations[1].pdf_url
locations[1].version
locations[1].raw_type	article
locations[1].license_id
locations[1].is_accepted	False
locations[1].is_published
locations[1].raw_source_name
locations[1].landing_page_url	https://doi.org/10.48550/arxiv.2405.17522
indexed_in	arxiv, datacite
authorships[0].author.id	https://openalex.org/A5100594585
authorships[0].author.orcid	https://orcid.org/0009-0001-0222-066X
authorships[0].author.display_name	Xi Zhu
authorships[0].author_position	first
authorships[0].raw_author_name	Zhu, Xi
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5013663621
authorships[1].author.orcid	https://orcid.org/0000-0003-2991-3703
authorships[1].author.display_name	Songcan Yu
authorships[1].author_position	middle
authorships[1].raw_author_name	Yu, Songcan
authorships[1].is_corresponding	False
authorships[2].author.id	https://openalex.org/A5109218676
authorships[2].author.orcid	https://orcid.org/0000-0002-6032-1274
authorships[2].author.display_name	Junbo Wang
authorships[2].author_position	middle
authorships[2].raw_author_name	Wang, Junbo
authorships[2].is_corresponding	False
authorships[3].author.id	https://openalex.org/A5071062179
authorships[3].author.orcid	https://orcid.org/0000-0002-7263-8914
authorships[3].author.display_name	Qinglin Yang
authorships[3].author_position	last
authorships[3].raw_author_name	Yang, Qinglin
authorships[3].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://arxiv.org/pdf/2405.17522
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	Efficient Model Compression for Hierarchical Federated Learning
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T06:51:31.235846
primary_topic.id	https://openalex.org/T10764
primary_topic.field.id	https://openalex.org/fields/17
primary_topic.field.display_name	Computer Science
primary_topic.score	0.9900000095367432
primary_topic.domain.id	https://openalex.org/domains/3
primary_topic.domain.display_name	Physical Sciences
primary_topic.subfield.id	https://openalex.org/subfields/1702
primary_topic.subfield.display_name	Artificial Intelligence
primary_topic.display_name	Privacy-Preserving Technologies in Data
related_works	https://openalex.org/W4391375266, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W4396696052, https://openalex.org/W2382290278, https://openalex.org/W4395014643
cited_by_count	0
locations_count	2
best_oa_location.id	pmh:oai:arXiv.org:2405.17522
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S4306400194
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	arXiv (Cornell University)
best_oa_location.source.host_organization	https://openalex.org/I205783295
best_oa_location.source.host_organization_name	Cornell University
best_oa_location.source.host_organization_lineage	https://openalex.org/I205783295
best_oa_location.license
best_oa_location.pdf_url	https://arxiv.org/pdf/2405.17522
best_oa_location.version	submittedVersion
best_oa_location.raw_type	text
best_oa_location.license_id
best_oa_location.is_accepted	False
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	http://arxiv.org/abs/2405.17522
primary_location.id	pmh:oai:arXiv.org:2405.17522
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S4306400194
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	arXiv (Cornell University)
primary_location.source.host_organization	https://openalex.org/I205783295
primary_location.source.host_organization_name	Cornell University
primary_location.source.host_organization_lineage	https://openalex.org/I205783295
primary_location.license
primary_location.pdf_url	https://arxiv.org/pdf/2405.17522
primary_location.version	submittedVersion
primary_location.raw_type	text
primary_location.license_id
primary_location.is_accepted	False
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	http://arxiv.org/abs/2405.17522
publication_date	2024-05-27
publication_year	2024
referenced_works_count	0
abstract_inverted_index.a	30, 92, 115, 133
abstract_inverted_index.FL	50, 74, 95, 103, 174
abstract_inverted_index.In	24
abstract_inverted_index.To	81
abstract_inverted_index.We	107
abstract_inverted_index.an	4, 109
abstract_inverted_index.as	3, 68
abstract_inverted_index.of	101
abstract_inverted_index.to	14, 17, 125, 172
abstract_inverted_index.(LC	138
abstract_inverted_index.and	39, 56, 85, 104, 118
abstract_inverted_index.are	52
abstract_inverted_index.but	165
abstract_inverted_index.due	13
abstract_inverted_index.for	54
abstract_inverted_index.has	9
abstract_inverted_index.its	15
abstract_inverted_index.not	159
abstract_inverted_index.our	156
abstract_inverted_index.raw	45
abstract_inverted_index.the	72, 99, 149
abstract_inverted_index.also	166
abstract_inverted_index.core	116, 130
abstract_inverted_index.each	129
abstract_inverted_index.edge	57
abstract_inverted_index.from	145
abstract_inverted_index.into	122
abstract_inverted_index.only	160
abstract_inverted_index.over	64
abstract_inverted_index.same	150
abstract_inverted_index.than	44
abstract_inverted_index.that	97, 113, 155
abstract_inverted_index.this	83, 89
abstract_inverted_index.with	136
abstract_inverted_index.(FL),	2
abstract_inverted_index.after	141
abstract_inverted_index.data,	46
abstract_inverted_index.local	37, 134
abstract_inverted_index.model	34, 41, 69, 105
abstract_inverted_index.novel	93
abstract_inverted_index.other	146
abstract_inverted_index.paper	90
abstract_inverted_index.share	40
abstract_inverted_index.sizes	70
abstract_inverted_index.their	36
abstract_inverted_index.these	25
abstract_inverted_index.train	29
abstract_inverted_index.using	35
abstract_inverted_index.where	60
abstract_inverted_index.affirm	154
abstract_inverted_index.client	117, 131
abstract_inverted_index.energy	169
abstract_inverted_index.mobile	55
abstract_inverted_index.models	144
abstract_inverted_index.neural	32
abstract_inverted_index.occurs	63
abstract_inverted_index.rather	43
abstract_inverted_index.reduce	168
abstract_inverted_index.within	20, 148
abstract_inverted_index.address	82
abstract_inverted_index.clients	27, 121, 147
abstract_inverted_index.consume	77
abstract_inverted_index.enhance	126
abstract_inverted_index.improve	86
abstract_inverted_index.network	33
abstract_inverted_index.present	108
abstract_inverted_index.privacy	19
abstract_inverted_index.results	153
abstract_inverted_index.systems	51
abstract_inverted_index.unified	31
abstract_inverted_index.accuracy	164
abstract_inverted_index.adaptive	110
abstract_inverted_index.benefits	100
abstract_inverted_index.capacity	16
abstract_inverted_index.cluster.	151
abstract_inverted_index.datasets	38
abstract_inverted_index.designed	53
abstract_inverted_index.emerging	5
abstract_inverted_index.existing	173
abstract_inverted_index.garnered	10
abstract_inverted_index.learning	1, 7, 22
abstract_inverted_index.maintain	161
abstract_inverted_index.preserve	18
abstract_inverted_index.privacy.	48
abstract_inverted_index.proposed	157
abstract_inverted_index.relative	171
abstract_inverted_index.systems,	26
abstract_inverted_index.systems.	23
abstract_inverted_index.training	61
abstract_inverted_index.wireless	65
abstract_inverted_index.Federated	0
abstract_inverted_index.algorithm	112, 140
abstract_inverted_index.attention	12
abstract_inverted_index.challenge	84
abstract_inverted_index.clustered	102
abstract_inverted_index.clusters.	123
abstract_inverted_index.computing	58
abstract_inverted_index.enhancing	47
abstract_inverted_index.framework	96
abstract_inverted_index.increase,	71
abstract_inverted_index.networks.	66
abstract_inverted_index.organizes	120
abstract_inverted_index.paradigm,	8
abstract_inverted_index.typically	62
abstract_inverted_index.Simulation	152
abstract_inverted_index.algorithms	158
abstract_inverted_index.clustering	111
abstract_inverted_index.collecting	142
abstract_inverted_index.comparable	162
abstract_inverted_index.compressed	143
abstract_inverted_index.frameworks	75
abstract_inverted_index.identifies	114
abstract_inverted_index.implements	132
abstract_inverted_index.integrates	98
abstract_inverted_index.introduces	91
abstract_inverted_index.parameters	42
abstract_inverted_index.predictive	163
abstract_inverted_index.resources.	80
abstract_inverted_index.aggregation	135
abstract_inverted_index.compression	137
abstract_inverted_index.consumption	170
abstract_inverted_index.distributed	21
abstract_inverted_index.dynamically	119
abstract_inverted_index.efficiency,	88, 128
abstract_inverted_index.mechanisms.	175
abstract_inverted_index.significant	11
abstract_inverted_index.substantial	78
abstract_inverted_index.Furthermore,	124
abstract_inverted_index.aggregation)	139
abstract_inverted_index.compression.	106
abstract_inverted_index.conventional	73
abstract_inverted_index.environments	59
abstract_inverted_index.hierarchical	94
abstract_inverted_index.increasingly	76
abstract_inverted_index.transmission	127
abstract_inverted_index.Consequently,	67
abstract_inverted_index.collaborative	6
abstract_inverted_index.communication	79, 87
abstract_inverted_index.significantly	167
abstract_inverted_index.Predominantly,	49
abstract_inverted_index.collaboratively	28
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile