Model training method and device based on longitudinal federated learning system and storage medium
The embodiment of the invention discloses a model training method and device based on a longitudinal federated learning system and a storage medium. The method comprises: executing an objective function of a model to be trained, wherein the model to be trained comprises at least two types of model p...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | XIA ZHENGXUN YANG YIFAN |
description | The embodiment of the invention discloses a model training method and device based on a longitudinal federated learning system and a storage medium. The method comprises: executing an objective function of a model to be trained, wherein the model to be trained comprises at least two types of model parameter sets, and each model parameter set corresponds to a matched training data set and/or training data label set; analyzing each data item included in the target function layer by layer to obtain a logic plan execution tree; generating a physical execution plan according to the training data set and/or the training data label set used by each tree node in the logic plan execution tree; and according to the physical execution plan, scheduling each device in the longitudinal federated learning system to train each model parameter set included in the to-be-trained model. According to the scheme of the embodiment of the invention, the model training process of longitudinal federation learning does not need to be c |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN112001500A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN112001500A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN112001500A3</originalsourceid><addsrcrecordid>eNqNjDEKwkAQRdNYiHqH8QDCRvEAEhQbrezDmPmJC5vZsDsRvL0heACrX7z3_rJoblEQyBJ79dpRD3tFIVYhwds3oCdnCEWlELXzNopXDtRCkNgmEsBpTvMnG_o5zRYTd5jexI_9uli0HDI2v10V28v5UV13GGKNPHADhdXVvSz3zpVH506Hf5wvkcc-Vw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Model training method and device based on longitudinal federated learning system and storage medium</title><source>esp@cenet</source><creator>XIA ZHENGXUN ; YANG YIFAN</creator><creatorcontrib>XIA ZHENGXUN ; YANG YIFAN</creatorcontrib><description>The embodiment of the invention discloses a model training method and device based on a longitudinal federated learning system and a storage medium. The method comprises: executing an objective function of a model to be trained, wherein the model to be trained comprises at least two types of model parameter sets, and each model parameter set corresponds to a matched training data set and/or training data label set; analyzing each data item included in the target function layer by layer to obtain a logic plan execution tree; generating a physical execution plan according to the training data set and/or the training data label set used by each tree node in the logic plan execution tree; and according to the physical execution plan, scheduling each device in the longitudinal federated learning system to train each model parameter set included in the to-be-trained model. According to the scheme of the embodiment of the invention, the model training process of longitudinal federation learning does not need to be c</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201127&DB=EPODOC&CC=CN&NR=112001500A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25544,76293</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201127&DB=EPODOC&CC=CN&NR=112001500A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>XIA ZHENGXUN</creatorcontrib><creatorcontrib>YANG YIFAN</creatorcontrib><title>Model training method and device based on longitudinal federated learning system and storage medium</title><description>The embodiment of the invention discloses a model training method and device based on a longitudinal federated learning system and a storage medium. The method comprises: executing an objective function of a model to be trained, wherein the model to be trained comprises at least two types of model parameter sets, and each model parameter set corresponds to a matched training data set and/or training data label set; analyzing each data item included in the target function layer by layer to obtain a logic plan execution tree; generating a physical execution plan according to the training data set and/or the training data label set used by each tree node in the logic plan execution tree; and according to the physical execution plan, scheduling each device in the longitudinal federated learning system to train each model parameter set included in the to-be-trained model. According to the scheme of the embodiment of the invention, the model training process of longitudinal federation learning does not need to be c</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjDEKwkAQRdNYiHqH8QDCRvEAEhQbrezDmPmJC5vZsDsRvL0heACrX7z3_rJoblEQyBJ79dpRD3tFIVYhwds3oCdnCEWlELXzNopXDtRCkNgmEsBpTvMnG_o5zRYTd5jexI_9uli0HDI2v10V28v5UV13GGKNPHADhdXVvSz3zpVH506Hf5wvkcc-Vw</recordid><startdate>20201127</startdate><enddate>20201127</enddate><creator>XIA ZHENGXUN</creator><creator>YANG YIFAN</creator><scope>EVB</scope></search><sort><creationdate>20201127</creationdate><title>Model training method and device based on longitudinal federated learning system and storage medium</title><author>XIA ZHENGXUN ; YANG YIFAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN112001500A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>XIA ZHENGXUN</creatorcontrib><creatorcontrib>YANG YIFAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XIA ZHENGXUN</au><au>YANG YIFAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Model training method and device based on longitudinal federated learning system and storage medium</title><date>2020-11-27</date><risdate>2020</risdate><abstract>The embodiment of the invention discloses a model training method and device based on a longitudinal federated learning system and a storage medium. The method comprises: executing an objective function of a model to be trained, wherein the model to be trained comprises at least two types of model parameter sets, and each model parameter set corresponds to a matched training data set and/or training data label set; analyzing each data item included in the target function layer by layer to obtain a logic plan execution tree; generating a physical execution plan according to the training data set and/or the training data label set used by each tree node in the logic plan execution tree; and according to the physical execution plan, scheduling each device in the longitudinal federated learning system to train each model parameter set included in the to-be-trained model. According to the scheme of the embodiment of the invention, the model training process of longitudinal federation learning does not need to be c</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN112001500A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
title | Model training method and device based on longitudinal federated learning system and storage medium |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T20%3A21%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=XIA%20ZHENGXUN&rft.date=2020-11-27&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN112001500A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |