Fast AI Model Splitting over Edge Networks

• URL: http://arxiv.org/abs/2507.01041v2
• Date: Thu, 03 Jul 2025 02:01:58 GMT
• Title: Fast AI Model Splitting over Edge Networks
• Authors: Zuguang Li, Wen Wu, Shaohua Wu, Songge Zhang, Ye Wang, Xuemin, Shen,
• Abstract summary: Complex AI model architectures pose high computational complexity to obtain the optimal model splitting.<n>We propose a fast DAG-based model splitting algorithm, which restructures the DAG to enable the optimal model splitting identification.<n>Considering AI models with block structures, we propose a block-wise model splitting algorithm to reduce computational complexity.
• Score: 20.38061058476494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Split learning (SL) has emerged as a computationally efficient approach for artificial intelligence (AI) model training, which can alleviate device-side computational workloads. However, complex AI model architectures pose high computational complexity to obtain the optimal model splitting. In this paper, we represent an arbitrary AI model as a directed acyclic graph (DAG), and then reformulate the optimal model splitting problem as a minimum s-t cut search problem. To solve the problem, we propose a fast DAG-based model splitting algorithm, which restructures the DAG to enable the optimal model splitting identification via a maximum flow method. Theoretical analysis indicates that the proposed algorithm is optimal. Furthermore, considering AI models with block structures, we propose a block-wise model splitting algorithm to reduce computational complexity. The algorithm abstracts each block, i.e., a component consisting of multiple layers, into a single vertex, thereby obtaining the optimal model splitting via a simplified DAG. Extensive experimental results demonstrate that the proposed algorithms can determine the optimal model splitting within milliseconds, as well as reduce training delay by 24.62%-38.95% in dynamic edge networks as compared to the state-of-the-art benchmarks.

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