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Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

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arxiv 2406.02616 v5 pith:ODXD7XOH submitted 2024-06-03 cs.LG cs.AI

Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Yuxuan Chen , Rongpeng Li , Xiaoxue Yu , Zhifeng Zhao , Honggang Zhang This is my paper
classification cs.LG cs.AI
keywords edgecomputationalcomputinginferencesplittingapproachdeploymentlearning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency. Toward efficient wireless LLM inference in edge computing, this study comprehensively analyzes the impact of different splitting points in mainstream open-source LLMs. On this basis, this study introduces a framework taking inspiration from model-based reinforcement learning (MBRL) to determine the optimal splitting point across the edge and user equipment (UE). By incorporating a reward surrogate model, our approach significantly reduces the computational cost of frequent performance evaluations. Extensive simulations demonstrate that this method effectively balances inference performance and computational load under varying network conditions, providing a robust solution for LLM deployment in decentralized settings.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference

    cs.CR 2026-05 unverdicted novelty 6.0

    ActInv reconstructs client inputs from server-visible activations in LLM split inference despite common defenses, PAF quantifies per-layer leakage risk, and PriPert improves defenses via calibrated perturbations.

  2. WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching

    cs.DC 2026-01 unverdicted novelty 6.0

    WISP suppresses wasted drafting time and verification interference in edge-cloud speculative LLM serving through dynamic drafting and SLO-aware batching, delivering up to 2.1x capacity and 1.94x goodput gains over cen...

  3. Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing

    cs.PF 2026-06 unverdicted novelty 4.0

    Enriched textual prompts lift local LLM accuracy on binary IoT environmental queries from 50.9-63.7% to 81.7-89.3% while preserving sub-second latency.

  4. Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI

    cs.DC 2025-11 reject novelty 4.0

    A framework for runtime re-splitting and re-placement of foundation model layers across edge nodes is proposed, but its claimed latency gains are inherited from prior work rather than measured.