Fast AI Model Partition for Split Learning over Edge Networks

[2507.01041] Fast AI Model Partition for Split Learning over Edge Networks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2507.01041 (cs) [Submitted on 23 Jun 2025 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: Fast AI Model Partition for Split Learning over Edge Networks Authors: Zuguang Li , Wen Wu , Shaohua Wu , Xuemin (Sherman) Shen View a PDF of the paper titled Fast AI Model Partition for Split Learning over Edge Networks, by Zuguang Li and 3 other authors View PDF HTML (experimental) Abstract: Split learning (SL) is a distributed learning paradigm that can enable computation-intensive artificial intelligence (AI) applications by partitioning AI models between mobile devices and edge servers. %fully utilizing distributed computing resources for computation-intensive mobile intelligence applications. However, the model partitioning problem in SL becomes challenging due to the diverse and complex architectures of AI models. In this paper, we formulate an optimal model partitioning problem to minimize training delay in SL. To solve the problem, we represent an arbitrary AI model as a directed acyclic graph (DAG), where the model's layers and inter-layer connections are mapped to vertices and edges, and training delays are captured as edge weights. Then, we propose a general model partitioning algorithm by transforming the problem into a minimum \textit{s-t} cut problem on the DAG. Theoretical analysis shows that the two problems are equivalent, such that the optimal model partition can be obtained via a maximum-flow method. Furthermore, taking AI models with block structures into consideration, we design a low-complexity block-wise model partitioning algorithm to determine the optimal model partition. Specifically, the algorithm simplifies the DAG by abstracting each block (i.e., a repeating component comprising multiple layers in an AI model) into a single vertex. Extensive experimental results on a hardware testbed equipped with NVIDIA Jetson devices demonstrate that the proposed solution can reduce algorithm running time by up to 13.0$\times$ and training delay by up to 38.95\%, compared to state-of-the-art baselines. Comments: This version lacks sufficient detail in key technical parts, including the equivalence proof for the s-t cut transformation and the computational complexity analysis (Sections VI-D). We are withdrawing it to prepare a revised, more complete manuscript Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2507.01041 [cs.LG] (or arXiv:2507.01041v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2507.01041 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zuguang Li [ view email ] [v1] Mon, 23 Jun 2025 07:14:04 UTC (6,936 KB) [v2] Thu, 3 Jul 2025 02:01:58 UTC (6,937 KB) [v3] Wed, 24 Dec 2025 01:45:18 UTC (1 KB) (withdrawn) [v4] Tue, 14 Apr 2026 01:25:01 UTC (6,486 KB) Full-text links: Access Paper: View a PDF of the paper titled Fast AI Model Partition for Split Learning over Edge Networks, by Zuguang Li and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-07 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• OrderMoE: An expert similarity driven distributed edge MoE inference#386805
• Advancing graph neural networks : addressing representation challenges in the vision domain#413324
• Unifying Graph Neural Networks Through a Common Layer Equation#620512
• Unifying Graph Neural Networks Through a Common Layer Equation#618833
• Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation#1123385