東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Federated Machine Learning in US Dis...

Rorvig, Aditi S.,

FindBook

Google Book

Amazon

博客來

Federated Machine Learning in US Disaster Recovery Cost Prediction /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Federated Machine Learning in US Disaster Recovery Cost Prediction // Aditi S Rorvig.
作者:	Rorvig, Aditi S.,
面頁冊數:	1 electronic resource (136 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
標題:	Engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31639864
ISBN:	9798346812050

Federated Machine Learning in US Disaster Recovery Cost Prediction /
Rorvig, Aditi S.,

Federated Machine Learning in US Disaster Recovery Cost Prediction /Aditi S Rorvig. - 1 electronic resource (136 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

This research demonstrates how federated learning can be applied to disaster Recovery cost prediction across three agencies: the Federal Emergency Management Agency (FEMA), Housing and Urban Development (HUD), and Small Business Administration (SBA). In the absence of a machine learning solution in disaster Recovery cost prediction, the model training process compared three methods of cost: data-specific cost prediction, central cost prediction, and federated cost prediction. Nine separate model types were applied to the data at the individual and central level. It was determined that Extreme Gradient Boosting (XGBoost) was the best fit for the data. In the federated model, XGBoost was applied to each of the two partitions in the client level and Federated Averaging (FedAvg) was applied at the server level. The results demonstrate the feasibility of federated learning as a tool in disaster Recovery cost prediction, while maintaining privacy preserving needs of major legislation required in federal data sharing. The results proposed a scalable model that can be used in time-sensitive disaster scenarios for data-driven decision making where a central model is not available due to administrative delay. This approach is novel in the idea that machine learning can be used for cost prediction and federated learning can alleviate impediments in data sharing in the federal government.

English

ISBN: 9798346812050Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Federal Emergency Management Agency

Federated Machine Learning in US Disaster Recovery Cost Prediction /
LDR:02908nmm a22004453i 4500 001 2438892
005 20260727135846.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798346812050
035 $a (MiAaPQD)AAI31639864
035 $a AAI31639864
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Rorvig, Aditi S., $e author. $3 3830323
245 1 0 $a Federated Machine Learning in US Disaster Recovery Cost Prediction / $c Aditi S Rorvig.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (136 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
500 $a Advisors: Clift, Jennifer Committee members: Blackford, Joseph; Campos, Enrique.
502 $b D.Engr. $c The George Washington University $d 2025.
520 $a This research demonstrates how federated learning can be applied to disaster Recovery cost prediction across three agencies: the Federal Emergency Management Agency (FEMA), Housing and Urban Development (HUD), and Small Business Administration (SBA). In the absence of a machine learning solution in disaster Recovery cost prediction, the model training process compared three methods of cost: data-specific cost prediction, central cost prediction, and federated cost prediction. Nine separate model types were applied to the data at the individual and central level. It was determined that Extreme Gradient Boosting (XGBoost) was the best fit for the data. In the federated model, XGBoost was applied to each of the two partitions in the client level and Federated Averaging (FedAvg) was applied at the server level. The results demonstrate the feasibility of federated learning as a tool in disaster Recovery cost prediction, while maintaining privacy preserving needs of major legislation required in federal data sharing. The results proposed a scalable model that can be used in time-sensitive disaster scenarios for data-driven decision making where a central model is not available due to administrative delay. This approach is novel in the idea that machine learning can be used for cost prediction and federated learning can alleviate impediments in data sharing in the federal government.
546 $a English
590 $a School code: 0075
650 4 $a Engineering. $3 586835
650 4 $a American studies. $3 2122720
653 $a Federal Emergency Management Agency
653 $a Housing and Urban Development
653 $a Small Business Administration
653 $a Machine learning
653 $a Recovery cost prediction
690 $a 0537
690 $a 0800
690 $a 0454
690 $a 0323
710 2 $a The George Washington University. $b Engineering Management. $e degree granting institution. $3 3770547
720 1 $a Clift, Jennifer $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-06B.
790 $a 0075
791 $a D.Engr.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31639864