Research review of federated learning algorithms
In recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related application.Federated learning concept was in...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2020-11-01
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| Series: | 大数据 |
| Subjects: | |
| Online Access: | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2020055 |
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| _version_ | 1850212448815022080 |
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| author | Jianzong WANG Lingwei KONG Zhangcheng HUANG Linjie CHEN Yi LIU Anxun HE Jing XIAO |
| author_facet | Jianzong WANG Lingwei KONG Zhangcheng HUANG Linjie CHEN Yi LIU Anxun HE Jing XIAO |
| author_sort | Jianzong WANG |
| collection | DOAJ |
| description | In recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related application.Federated learning concept was introduced into three different layers.The first layer introduced the definition,architecture,classification of federated learning and compared the federated learning with traditional distributed learning.The second layer presented comparison and analysis of federated learning algorithms from machine learning and deep learning aspects.The third layer separated federated learning optimization algorithms into three aspects to optimize federated learning algorithm through reducing communication cost,selecting proper clients and different aggregation method.Finally,the current research status and three main challenges on communication,heterogeneity of system and data to be solved were concluded,and the future prospects in federated learning domain were proposed. |
| format | Article |
| id | doaj-art-400f690f77dc44769da4f348ec539d4b |
| institution | OA Journals |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2020-11-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-400f690f77dc44769da4f348ec539d4b2025-08-20T02:09:21ZzhoChina InfoCom Media Group大数据2096-02712020-11-0162020055159537360Research review of federated learning algorithmsJianzong WANGLingwei KONGZhangcheng HUANGLinjie CHENYi LIUAnxun HEJing XIAOIn recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related application.Federated learning concept was introduced into three different layers.The first layer introduced the definition,architecture,classification of federated learning and compared the federated learning with traditional distributed learning.The second layer presented comparison and analysis of federated learning algorithms from machine learning and deep learning aspects.The third layer separated federated learning optimization algorithms into three aspects to optimize federated learning algorithm through reducing communication cost,selecting proper clients and different aggregation method.Finally,the current research status and three main challenges on communication,heterogeneity of system and data to be solved were concluded,and the future prospects in federated learning domain were proposed.http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2020055federated learning;algorithm optimization;big data;data privacy |
| spellingShingle | Jianzong WANG Lingwei KONG Zhangcheng HUANG Linjie CHEN Yi LIU Anxun HE Jing XIAO Research review of federated learning algorithms 大数据 federated learning;algorithm optimization;big data;data privacy |
| title | Research review of federated learning algorithms |
| title_full | Research review of federated learning algorithms |
| title_fullStr | Research review of federated learning algorithms |
| title_full_unstemmed | Research review of federated learning algorithms |
| title_short | Research review of federated learning algorithms |
| title_sort | research review of federated learning algorithms |
| topic | federated learning;algorithm optimization;big data;data privacy |
| url | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2020055 |
| work_keys_str_mv | AT jianzongwang researchreviewoffederatedlearningalgorithms AT lingweikong researchreviewoffederatedlearningalgorithms AT zhangchenghuang researchreviewoffederatedlearningalgorithms AT linjiechen researchreviewoffederatedlearningalgorithms AT yiliu researchreviewoffederatedlearningalgorithms AT anxunhe researchreviewoffederatedlearningalgorithms AT jingxiao researchreviewoffederatedlearningalgorithms |