mlsanityData to 14 Oct 2026

FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation

arXiv 2603.09046 ↗First posted 10 Mar 2026cs.CR cs.LG cs.OScited by 1

On the map: Efficient LLM inference and serving → Serving systems and request scheduling

Abstract (arXiv)

Device-side Large Language Models (LLMs) have witnessed explosive growth, offering higher privacy and availability compared to cloud-side LLMs. During LLM inference, both model weights and user data are valuable, and attackers may even compromise the OS kernel to steal them. ARM TrustZone is the de facto hardware-based isolation technology on mobile devices, used to protect sensitive applications from a compromised OS. However, protecting LLM inference with TrustZone incurs significant overhead due to its inflexible isolation of memory and the NPU. To address these challenges, this paper introduces FlexServe, a fast and secure LLM serving system for mobile devices. It first introduces a Flexible Resource Isolation mechanism to construct Flexible Secure Memory (Flex-Mem) and Flexible Secure NPU (Flex-NPU). Both memory pages and the NPU can be efficiently switched between unprotected and protected modes. Based on these mechanisms, FlexServe designs a fast and secure LLM inference framework within TrustZone's secure world. The LLM-Aware Memory Management and Secure Inference Pipeline are introduced to accelerate inference. A Multi-Model Scheduler is proposed to optimize multi-model workflows. We implement a prototype of FlexServe and compare it with two TrustZone-based strawman designs. The results show that FlexServe achieves an average 10.05× speedup in Time to First Token (TTFT) compared to the strawman, and an average 2.44× TTFT speedup compared to an optimized strawman with pipeline and secure NPU enabled. For multi-model agent workflows, the end-to-end speedup is up to 24.30× and 4.05× compared to the strawman and optimized strawman, respectively.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Serving systems and request scheduling, one of 12 directions in Efficient LLM inference and serving. Builds on 23 of its 80 references that are in the map. First posted on arXiv 10 Mar 2026.

Builds on

23 of 80 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Apple Intelligence Foundation Language Models: Tech Report 202517 Jul

Other references · 22

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2023Qwen Technical Reportcited by 3,344
  4. 2025Gemma 3 Technical Reportcited by 1,824
  5. 2024Phi-4 Technical Reportcited by 711
  6. 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 222
  7. 2023You Only Look at Screens: Multimodal Chain-of-Action Agentscited by 185
  8. 2024Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMscited by 176
  9. 2021Lawformer: A Pre-trained Language Model for Chinese Legal Long Documentscited by 116
  10. 2024On-Device Language Models: A Comprehensive Reviewcited by 104
  11. 2024The rising costs of training frontier AI modelscited by 95
  12. 2020DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environmentscited by 58
10 more
  1. 2024Ferret-UI 2: Mastering Universal User Interface Understanding Across Platformscited by 51
  2. 2025Characterizing Mobile SoC for Accelerating Heterogeneous LLM Inferencecited by 22
  3. 2025Position: The Most Expensive Part of an LLM should be its Training Datacited by 16
  4. 2020Serdab: An IoT Framework for Partitioning Neural Networks Computation across Multiple Enclavescited by 11
  5. 2024TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deploymentcited by 11
  6. 2025MobiAgent: A Systematic Framework for Customizable Mobile Agentscited by 9
  7. 2025UI-UG: A Unified MLLM for UI Understanding and Generationcited by 5
  8. 2025TZ-LLM: Protecting On-Device Large Language Models with Arm TrustZonecited by 5
  9. 2023Robust Safe Reinforcement Learning under Adversarial Disturbancescited by 4
  10. 2017TrustShadow: Secure Execution of Unmodified Applications with ARM TrustZonecited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
  2. 2023LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices
  3. 2026Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference
  4. 2025A Survey: Towards Privacy and Security in Mobile Large Language Models
  5. 2025AttestLLM: Efficient Attestation Framework for Billion-scale On-device LLMs
  6. 2026EdgeFlow: Fast Cold Starts for LLMs on Mobile Devices ↗
  7. 2026Securing LLMs in the Wild: Privacy and Security Challenges at the Edge
  8. 2025Towards Confidential and Efficient LLM Inference with Dual Privacy Protection