[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123420-en":3,"doc-seo-123420-105":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":4,"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},123420,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Towards the Network Systems of Decentralized Machine Learning - Doctoral Dissertation","This thesis addresses critical challenges in decentralized federated learning (DFL) related to network efficiency, practical deployment, security, and scalability to advanced models. It introduces near-optimal overlay networks, proposes theory and designs for DFedAvg, and develops practical protocols in systems such as FedLay. The work also presents secure decentralized learning mechanisms with blockchain and a privacy-preserving scalable overlay, PlanetServe, for serving large language models.","UC Santa Cruz  \nUC Santa Cruz Electronic Theses and Dissertations  \nTitle  \nTowards the Network Systems of Decentralized Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/9324s81m](https://escholarship.org/uc/item/9324s81m)  \nISBN  \n9798293860821  \nAuthor  \nHua, Yifan  \nPublication Date  \n2025-06-23  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nSANTA CRUZ  \nTOWARDS THE NETWORK SYSTEMS OF DECENTRALIZED  \nMACHINE LEARNING  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin  \nCOMPUTER SCIENCE AND ENGINEERING  \nby  \nYifan Hua  \nJune 2025  \nThe Dissertation of Yifan Hua  \nis approved:  \n\n| Professor Chen Qian, Chair |\n| --- |\n| Professor Katia Obraczka |\n\nProfessor Yang Liu  \nPeter Biehl  \nDean of Graduate Studies  \nCopyright © by Yifan Hua  \n2025  \nTable of Contents  \nAbstract vi Acknowledgments vii 1 Introduction 1  \n2 Efficient and Reliable Overlay Networks for Decentralized Federated  \nLearning 5  \n2.1 Introduction .................................. 5  \n2.1.1 Our contribution ........................... 7  \n2.1.2 Additional related works ....................... 8  \n2.1.3 Notations ............................... 10  \n2.2 Theory of DFedAvg ............................. 10  \n2.2.1 Convergence of DFedAvgM ..................... 12  \n2.2.2 Generalization of DFedAvgM .................... 14  \n2.3 Network Topology Design .......................... 17  \n2.3.1 Comparison to Ring and Erd¨os-R´enyi graphs ........... 23  \n2.4 Practical network design ........................... 25  \n2.4.1 Network recovery from node failures ................ 26  \n2.5 Experimental Results ............................. 27  \n2.5.1 Convergence and generalization ................... 27  \n2.5.2 Robustness to client failures ..................... 32  \n2.6 Concluding Remarks ............................. 35  \n3 FedLay: Towards Practical Overlay Networks for Decentralized Federated Learning 36  \n3.1 Introduction .................................. 36  \n3.2 Overlay Topology of FedLay ......................... 40  \n3.2.1 Three metrics for DFL topologies .................. 41  \n3.2.2 FedLay topology ........................... 43  \n3.3 Overlay Topology of FedLay ......................... 47  \n3.3.1 Three metrics for DFL topologies .................. 47  \niii  \n3.3.2 FedLay topology ........................... 49  \n3.4 Design of FedLay Protocols ......................... 54  \n3.4.1 Overview ............................... 54  \n3.4.2 Neighbor Discovery and Maintenance Protocols (NDMP) .... 54  \n3.4.3 Model Exchange Protocol (MEP) .................. 62  \n3.5 Performance Evaluation ........................... 65  \n3.5.1 Evaluation methodology ....................... 65  \n3.5.2 Evaluation of FedLay topology ................... 69  \n3.5.3 DFL model accuracy ......................... 70  \n3.5.4 Evaluation of other considerations ................. 72  \n3.5.5 Scalability ............................... 73  \n3.6 Related Works ................................ 74  \n3.7 Conclusion .................................. 75  \n4 Secure Decentralized Learning with Blockchain 78  \n4.1 Introduction .................................. 78  \n4.2 Overview ................................... 82  \n4.2.1 Network Model ............................ 82  \n4.2.2 Blockchain Model and Assumptions ................ 83  \n4.2.3 Attacker Model ............................ 83  \n4.2.4 Requirements ............................. 84  \n4.3 Design Overview ............................... 85  \n4.4 Protocol Design ................................ 89  \n4.4.1 Topology","cbCaihrncSQxPH1V","https://ap.wps.com/l/cbCaihrncSQxPH1V","pdf",4034212,1,187,"English","en",105,"# Abstract\n# Introduction\n## Efficient and Reliable Overlay Networks for Decentralized Federated Learning\n## FedLay: Towards Practical Overlay Networks for Decentralized Federated Learning\n## Secure Decentralized Learning with Blockchain\n## PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving\n# Conclusion","[{\"question\":\"What main problem does the thesis target in decentralized federated learning?\",\"answer\":\"It targets network efficiency, practical deployment, security, and scalability in decentralized federated 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