CIDERS 云边协同 LLM 个性化双层优化框架论文因实验问题撤回
CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization
AI 导读
作者 Yi Yuan 撤回了提出 CIDERS 云边协同大语言模型学习框架的论文，称实验存在多个关键问题，需尽快撤稿以避免误导。该框架此前报告在压缩边缘路径上，数学推理和代码生成分别取得 3.1x 和 1.7x 提升，指令指标相对提升 10%。
正文 · 原文
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Abstract:Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap for practical LLM deployment. However, existing cloud-edge paradigms struggle to balance global consensus with local personalization, which fails to satisfy the need for a unified knowledge foundation on the cloud and domain-specific adaptation at the edge. To address this, we introduce, for the first time, a personalized bilevel optimization framework that formalizes cloud-edge LLM collaboration as a dual structure: the upper level optimizes edge-side personalization, while the lower level governs cloud-side knowledge transfer, reaching cloud-edge evolving in coordination. We then propose CIDERS, an efficient solver that decomposes the model into a learnable backbone and a messenger. While the cloud performs knowledge transfer to the learnable backbone, the key lies in embedding global trajectories into each local personalization step via consensus-variate correction to reconcile personalization with consensus. We provide a comprehensive theoretical analysis, including a geometric characterization of the local trajectory and a full convergence guarantee, revealing an explicit trade-off structure between personalization and global convergence. Extensive experiments demonstrate that CIDERS consistently outperforms competitive baselines on the compressed edge path, with 3.1x and 1.7x gains on mathematical reasoning and code generation, respectively, and a 10\% relative gain on instruction metrics. Mechanism experiments attribute these gains to early consensus-corrected coordination and task-aware distillation. Overall, CIDERS offers a viable path toward consensus-guided continuous personalization in cloud-edge LLM systems.
| Comments: | There are several key inssues in our experiment that we must withdraw as soon as possible to avoid any misleading |
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.15664 [cs.DC] |
| (or arXiv:2609.15664v3 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2609.15664 arXiv-issued DOI via DataCite |
Submission history
From: Yi Yuan Dr [view email]
[v1]
Mon, 14 Sep 2026 14:45:24 UTC (1,468 KB)
[v2]
Sun, 8 Nov 2026 02:15:28 UTC (1 KB) (withdrawn)
[v3]
Tue, 10 Nov 2026 07:53:02 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org