{"data":{"id":"10.4121/557749ca-510d-4717-adb4-20bd456cc9f4","type":"dois","attributes":{"doi":"10.4121/557749ca-510d-4717-adb4-20bd456cc9f4","prefix":"10.4121","suffix":"557749ca-510d-4717-adb4-20bd456cc9f4","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Cox, Bart","nameType":"Personal","givenName":"Bart","familyName":"Cox","nameIdentifiers":[{"nameIdentifier":"0000-0001-5209-6161","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Code supporting the publication: Nomad: Accelerating Geo-distributed Learning with Client Transfers"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Distributed Computing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0805"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"federated learning"},{"subject":"client-churn"},{"subject":"resource heterogeneity"},{"subject":"multi-server"},{"subject":"asynchronous learning"}],"contributors":[{"name":"TU Delft, Faculty of Electrical Engineering","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"Mathematics and Computer Science, Department of Software Technology","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2026-06-02","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"Software","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1109/nca67271.2025.00014","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/557749ca-510d-4717-adb4-20bd456cc9f4.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["text/x-python"],"version":"1","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository contains the source code and simulation framework used in the publication \u003cem\u003e\"Nomad: Accelerating Geo-distributed Learning with Client Transfers\"\u003c/em\u003e. The framework enables experimentation with asynchronous multi-server Federated Learning (FL) in geo-distributed environments, supporting dynamic client-to-server reassignment, multiple aggregation strategies, heterogeneous client data distributions, varying network latencies, and client churn scenarios. Nomad implements adaptive client transfer mechanisms that optimize server assignments based on both communication latency and data distribution characteristics, facilitating the evaluation of training efficiency, convergence behavior, and model accuracy under realistic geo-distributed FL 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