[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122541-en":3,"doc-seo-122541-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122541,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Optimising resource utilisation in collaborative machine learning systems","This thesis investigates how to optimize resource utilisation in collaborative machine learning systems, focusing on efficient training and communication across distributed participants. It presents contributions spanning pipeline parallelism for collaborative machine learning, collaborative training with parallel operations, and resource-utilization optimized federated learning. The research also develops approaches for communication-efficient and high-accuracy split federated learning, alongside methods such as federated learning with increased resource utilization and unidirectional inter-block training. Published and under-review works are documented through journal articles, patents, and arXiv preprints.","Optimising resource utilisation in collaborative machine learning systems  \nZihan Zhang  \nA thesis submitted for the degree of PhD  \nat the University of St Andrews  \n2026  \nFull metadata for this thesis is available in St Andrews Research Repository at:  \n[https://research-repository.st-andrews.ac.uk/](https://research-repository.st-andrews.ac.uk/)  \n[Identifier to use to cite or link to this thesis:](Identifier to use to cite or link to this thesis:)[ ](Identifier to use to cite or link to this thesis:)DOI: [https://doi.org/10.17630/sta/1534](https://doi.org/10.17630/sta/1534)  \nThis item is protected by original copyright  \nThis item is licensed under a Creative Commons Licence  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nDeclaration  \nCandidate’s Declarations  \nI, Zihan Zhang, do hereby certify that this thesis, submitted for the degree of PhD, which is approximately 59,000 words in length, has been written by me, and that it is the record of work carried out by me, or principally by myself in collaboration with others as acknowledged, and that it has not been submitted in any previous application for any degree. I confirm that any appendices included in my thesis contain only material permitted by the ‘Assessment of Postgraduate Research Students’ policy.  \nI was admitted as a research student at the University of St Andrews in January 2022.  \nI received funding from an organisation or institution and have acknowledged the funder(s) in the full text of my thesis.  \nDate: 22/07/2025  \nSignature of candidate:  \nSupervisor’s Declaration  \nI hereby certify that the candidate has fulfilled the conditions of the Resolution and Regulations appropriate for the degree of Doctor of Philosophy in the University of St Andrews and that the candidate is qualified to submit this thesis in application for that degree. I confirm that any appendices included in my thesis contain only material permitted by the ‘Assessment of Postgraduate Research Students’ policy.  \nDate: 22/07/2025  \nSignature of supervisor:  \nPermission for Electronic Publication  \nIn submitting this thesis to the University of St Andrews we understand that weare giving permission for it to be made available for use in accordance with the regulations of the University Library for the time being in force, subject to any copyright vested in the work not being affected thereby. We also understand, unless exempt by an award of an embargo as requested below, that the title and the abstract will be published, and that a copy of the work may be made and supplied to any bona fide library or research worker, that this thesis will be electronically accessible for personal or research use and that the library has the right to migrate this thesis into new electronic forms as required to ensure continued access to the thesis.  \nI, Zihan Zhang, confirm that my thesis does not contain any third-party material that requires copyright clearance.  \nThe following is an agreed request by candidate and supervisor regarding the publication of this thesis:  \nElectronic Copy  \nNo embargo on electronic copy.  \nDate: 22/07/2025  \nSignature of candidate:  \nSignature of supervisor:  \nUnderpinning Research Data or Digital Outputs  \nCandidate’s declaration  \nI, Zihan Zhang, hereby certify that no requirements to deposit original research data or digital outputs apply to this thesis and that, where appropriate, secondary data used have been referenced in the full text of my thesis.  \nDate: 22/07/2025  \nSignature of candidate:  \nPublications  \nThis dissertation includes work that has led to published or under-review articles and patents, as outlined below.  \nPublished  \n1. Zihan Zhang, Philip Rodgers, Peter Kilpatrick, Ivor Spence, Blesson Varghese. PiPar: Pipeline Parallelism for Collaborative Machine Learning. Journal of Parallel and Distributed Computing, Vol. 193, Article 104947, 2024 .  \n2. Zihan Zhang, Blesson Varghese, Philip Rodgers, Ivor Spenc","cbCaiflKdTxQTwuR","https://ap.wps.com/l/cbCaiflKdTxQTwuR","pdf",9802272,1,213,"English","en",105,"# Declaration and permissions\n## Candidate’s declarations\n## Supervisor’s declaration\n## Permission for electronic publication\n## Underpinning research data or digital outputs\n## Publications","[{\"question\":\"What is the main research focus of the thesis?\",\"answer\":\"The thesis focuses on optimizing resource utilisation in collaborative machine learning systems, improving efficiency across distributed training settings.\"},{\"question\":\"Which federated learning approaches are covered?\",\"answer\":\"It covers resource-utilization optimized federated learning, communication-efficient split federated learning, and federated learning strategies such as increased resource utilisation and unidirectional inter-block training.\"},{\"question\":\"How is the thesis related to published and ongoing work?\",\"answer\":\"The document lists published items and under-review arXiv preprints and journal submissions, along with patents and patent applications that correspond to the thesis contributions.\"}]","Optimising resource utilisation in collaborative machine learning systems | 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