AI RESEARCH
Fast AI Model Partition for Split Learning over Edge Networks
arXiv CS.AI
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ArXi:2507.01041v4 Announce Type: replace-cross 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