Cover Image

Priority-Aware Model-Distributed Inference at Edge Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Teng, Seferoglu, Hulya
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Early-Exit meets Model-Distributed Inference at Edge Networks
by: Colocrese, Marco, et al.
Published: (2024)

DIGEST: Fast and Communication Efficient Decentralized Learning with Local Updates
by: Gholami, Peyman, et al.
Published: (2023)

Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference
by: Siavashi, Mohammad, et al.
Published: (2025)

Intelligent Orchestration of Distributed Large Foundation Model Inference at the Edge
by: Koch, Fernando, et al.
Published: (2025)

ChunkFlow: Communication-Aware Chunked Prefetching for Layerwise Offloading in Distributed Diffusion Transformer Inference
by: Meng, Han, et al.
Published: (2026)

Distributed Convolutional Neural Network Training on Mobile and Edge Clusters
by: Rama, Pranav, et al.
Published: (2024)

Robust Federated Learning against Model Perturbation in Edge Networks
by: Jin, Dongzi, et al.
Published: (2025)

DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
by: Peng, Xian, et al.
Published: (2025)

Preemption Aware Task Scheduling for Priority and Deadline Constrained DNN Inference Task Offloading in Homogeneous Mobile-Edge Networks
by: Cotter, Jamie, et al.
Published: (2025)

Fast Distributed Inference Serving for Large Language Models
by: Wu, Bingyang, et al.
Published: (2023)

A Survey on Collaborative DNN Inference for Edge Intelligence
by: Ren, Weiqing, et al.
Published: (2022)

DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Serving
by: Yu, Fengze, et al.
Published: (2025)

Adaptive Stream Processing on Edge Devices through Active Inference
by: Sedlak, Boris, et al.
Published: (2024)

Learning the Optimal Path and DNN Partition for Collaborative Edge Inference
by: Huang, Yin, et al.
Published: (2024)

NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning
by: Wang, Irene, et al.
Published: (2026)

Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks
by: Deng, Xiumei, et al.
Published: (2025)

Optimizing Distributed Deployment of Mixture-of-Experts Model Inference in Serverless Computing
by: Liu, Mengfan, et al.
Published: (2025)

Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
by: Yu, Lu, et al.
Published: (2024)

Multi-DNN Inference of Sparse Models on Edge SoCs
by: Luo, Jiawei, et al.
Published: (2026)

Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks
by: Atapour, S. Kawa, et al.
Published: (2024)

TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks
by: Qiang, Xianke, et al.
Published: (2026)

Towards Integrated Fine-tuning and Inference when Generative AI meets Edge Intelligence
by: Chen, Ning, et al.
Published: (2024)

MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines
by: Gao, Lei, et al.
Published: (2024)

Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
by: Zhang, Zhenxiao, et al.
Published: (2024)

MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference
by: Nikolaidis, Sokratis, et al.
Published: (2024)

ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
by: Oh, Hyungjun, et al.
Published: (2024)

RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models
by: Zheng, Zihao, et al.
Published: (2026)

Lightweight Federated Learning over Wireless Edge Networks
by: Hou, Xiangwang, et al.
Published: (2025)

Modular Foundation Model Inference at the Edge: Network-Aware Microservice Optimization
by: Zhu, Juan, et al.
Published: (2026)

Split CNN Inference on Networked Microcontrollers
by: Lu, Junyu, et al.
Published: (2026)

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
by: Bai, Yuxuan, et al.
Published: (2025)

Deal: Distributed End-to-End GNN Inference for All Nodes
by: Chen, Shiyang, et al.
Published: (2025)

LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load
by: Tummalapalli, Pranay, et al.
Published: (2026)

Harli: SLO-Aware Co-location of LLM Inference and PEFT-based Finetuning on Model-as-a-Service Platforms
by: Xu, Ao, et al.
Published: (2025)

DALI: A Workload-Aware Offloading Framework for Efficient MoE Inference on Local PCs
by: Zhu, Zeyu, et al.
Published: (2026)

Optimizing Performance on Trinity Utilizing Machine Learning, Proxy Applications and Scheduling Priorities
by: Romero, Phil
Published: (2024)

Accelerating Local LLMs on Resource-Constrained Edge Devices via Distributed Prompt Caching
by: Matsutani, Hiroki, et al.
Published: (2026)

TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference
by: Gond, Raja, et al.
Published: (2025)

CoFormer: Collaborating with Heterogeneous Edge Devices for Scalable Transformer Inference
by: Xu, Guanyu, et al.
Published: (2025)

DisCEdge: Distributed Context Management for Large Language Models at the Edge
by: Malekabbasi, Mohammadreza, et al.
Published: (2025)