Structured Pruning

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3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

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Latest papers 37

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FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices
Oct 1, 2026Wentao Yue, Tianyou Lai, Hongji Li +2Non-IID Federated Learning Structured Pruning
LILA: Calibration-Free Structured Pruning of Large Language Models via Latent Spectral Geometry
Sep 11, 2026Sankar Behera, Dhruv Singh, Anshika Agnihotri +3LLM Pruning LLM Compression
CSE, IIT Jammu · Mathematics, IIT Jammu · IT, EC Bikaner +1
Dense Structural Compression of Transformers via Gauge-Correct Channel Removal
Sep 7, 2026Jed A. Duersch, Naïm Es-Sebbani, Nathanaël Haas +1Efficient Transformer Inference Structured Pruning
Université d’Artois, CNRS, CRIL UMR 8188, Lens, France
Functional Degeneracy in Neural Networks: Measurement and Pruning
Aug 31, 2026Maria Matveev, Pascal Esser, Ayush Bharadwaj +2Structured Pruning Neural Network Compression
Department of Mathematics, LMU Munich · Munich Center for Machine Learning (MCML) · Independent +3
PruneShift: A Framework for Evaluating Decision Reliability in Structured Pruning
Aug 30, 2026Hao Ye, Gaopeng ZhangSurrogate Modeling Structured Pruning
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences · University of Chinese Academy of Sciences
Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization
Aug 13, 2026Palaash Goel, Ayan Sengupta, Akshay Nambi +1LLM Pruning Structured Pruning
Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi · Department of Electrical Engineering, Indian Institute of Technology Delhi · Microsoft Research (MSR) India
InterPruner: Interactive Structured Pruning via Taylor-Implicit Criterion and Language-Prior Modulator for Multimodal Object Detection
Aug 11, 2026Qi Ming, Zihan Yang, Shaoguang Huang +6Multispectral Object Detection Infrared Object Detection
Beijing University of Technology · Southwest University · China University of Geosciences Wuhan +5
StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning
Aug 5, 2026Timo Bartels, Danish Nazir, Jan Piewek +2Efficient Neural Network Inference Structured Pruning
Technische Universität Braunschweig, Germany · Group Innovation, Volkswagen AG, Wolfsburg, Germany
SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models
Aug 4, 2026Yaozhi Wen, Jialong Guo, Zhenliang Ni +2Efficient VLM Inference Structured Pruning
Huawei Technologies
CoCurve: Cross-Module Co-Pruning Curvature for Training-Free Structured LLM Pruning
Jul 20, 2026Zhiren Gong, Zihao Zeng, Zijie Wang +3LLM Pruning LLM Compression
College of Computing and Data Science, Nanyang Technological University, Singapore · Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention
Jul 9, 2026Ryota Kobayashi, Tsubasa Hirakawa, Takayoshi Yamashita +4LLM Pruning LLM Compression
1Chubu University · 2Panasonic Holdings Corporation
FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models
Jun 26, 2026Fan Mo, Yuxuan Han, Geng Zhang +2Mixture-of-Experts Pruning LLM Pruning
National University of Singapore
Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?
Jun 26, 2026Guoheng Sun, Kaixi Feng, Shwai He +8Model Compression Efficient VLA Models
University of Maryland, College Park · 2Cisco Research
Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT
Jun 25, 2026Jinghan Wang, Yanjun Chen, Wei Zhang +3LLM Pruning On-Device Language Model Inference
School of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China · School of Information Science and Technology, Eastern Institute of Technology, Ningbo, China
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
Jun 16, 2026Yifu Ding, Jiacheng Wang, Ge Yang +4Mixture-of-Experts Pruning LLM Pruning
State Key Laboratory of Complex & Critical Software Environment, Beihang University · School of Computer Science and Engineering, Beihang University · Nanyang Technological University +1
Squeeze-Release: Iterative Pruning with Exact Structural Minimization
Jun 12, 2026Roman Denkin, Ida Akerholm, Prashant Singh +1Structured Pruning Neural Network Compression
Department of Information Technology, Uppsala University, Uppsala, Sweden · Science for Life Laboratory, Uppsala University, Uppsala, Sweden
Small LLMs: Pruning vs. Training from Scratch
Jun 12, 2026Yufeng Xu, Taiming Lu, Kunjun Li +3Language Model Pretraining LLM Pruning
Princeton University · New York University · Carnegie Mellon University
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
Jun 8, 2026Haozhe Hu, Hao Wu, Anhao Zhao +4LLM Pruning LLM Inference Acceleration
Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Department of Computing, The Hong Kong Polytechnic University · Munich Center for Machine Learning, LMU Munich
Less is MoE: Trimming Experts in Domain-Specialist Language Models
Jun 4, 2026Haoze He, Xinkai Zou, Xuan Jiang +4Mixture-of-Experts Pruning LLM Pruning
Carnegie Mellon University · UCSD · MIT
TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts
Jun 3, 2026Jiangyang He, Shaolin Zhu, Deyi XiongMixture-of-Experts Pruning LLM Pruning
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China
PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers
Jun 2, 2026Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1Efficient ViTs Spiking Neural Networks
eBRAIN Lab, Division of Engineering, New York University (NYU) Abu Dhabi, United Arab Emirates (UAE)
PrunePath: Towards Highly Structured Sparse Language Models
May 27, 2026Zhexuan Gu, Zixun Fu, Yancheng YuanMixture-of-Experts Pruning LLM Pruning
Department of Applied Mathematics, The Hong Kong Polytechnic University
MuCRASP: Multimodal Chain-of-thought Reasoning aware Structured Pruning
May 25, 2026Aritra Dutta, Somak AdityaVision-Language Models Efficient VLM Inference
Indian Institute of Technology, Kharagpur
Pruning Deep Neural Networks via the Marchenko--Pastur Distribution
May 23, 2026Leonid Berlyand, Theo Bourdais, Houman Owhadi +1Structured Pruning Sparse Neural Networks
Department of Mathematics Pennsylvania State University University Park, PA 16802, USA · Department of Computing and Mathematical Sciences California Institute of Technology Pasadena, CA 91125, USA
GSA-YOLO: A High-Efficiency Framework via Structured Sparsity and Adaptive Knowledge Distillation for Real-Time X-ray Security Inspection
May 20, 2026Jiahao KongStructured Pruning Real-Time Object Detection
SDU-ANU Joint Science College, Shandong University, Weihai, 264200, China
Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference
May 19, 2026Beomseok Kang, Dongwon Jo, Jiwon Song +2Vision-Language Models Efficient VLM Inference
Seoul National University
Prune, Update and Trim: Robust Structured Pruning for Large Language Models
May 18, 2026Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidt-ThiemeLLM Pruning LLM Inference Acceleration
ISMLL & DARC VWFS · University of Hildesheim · Hildesheim, Germany +1
MedCore: Boundary-Preserving Medical Core Pruning for MedSAM
May 13, 2026Cenwei Zhang, Suncheng Xiang, Lei YouModel Compression Image Segmentation
Shanghai Jiao Tong University Shanghai, China · Technical University of Denmark Ballerup, Denmark
Relative Kinetic Utility: Calibrating Cross-Layer Credit for Global Structured LLM Pruning
May 9, 2026Tianhao Qian, Guilin Qi, Jiayu ChenLLM Pruning Structured Sparsity
School of Mathematics, Southeast University Nanjing 210096, China
Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning
May 9, 2026Chen Wang, Siyu Hu, Guangming Tan +1Equivariant GNNs Structured Pruning
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences
Task Relevance Is Not Local Replaceability: A Two-Axis View of Channel Information
May 8, 2026Houman Safaai, Andrew T. Landau, Celia C. Beron +2Structured Pruning Neural Network Pruning
Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University, Cambridge, MA 02138, USA · Department of Neurobiology, Howard Hughes Medical Institute Harvard Medical School, Boston, MA 02115, USA
SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation
May 4, 2026Ce Wang, Zhenyu Hu, Wanjie SunDiffusion Model Distillation Remote Sensing Image Super-Resolution
School of Remote Sensing and Information Engineering, Wuhan University; Wuhan 430079, China
GETA-3DGS: Automatic Joint Structured Pruning and Quantization for 3D Gaussian Splatting
May 3, 2026Baobing Zhang, Wanxin Sui3DGS Compression Mixed-Precision Quantization
Robotics Research Group, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom · Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, Uxbridge, Middlesex, UB8 3PH, United Kingdom
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Apr 27, 2026Yiran Huang, Lukas Thede, Massimiliano Mancini +2Large Vision-Language Models Structured Pruning
Technical University of Munich, Germany · Munich Center for Machine Learning, Germany. · Helmholtz Munich, Germany. +3
GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models
Apr 21, 2026Ziyang Wang, Jiangfeng Xiao, Chuan Xiao +3LLM Pruning LLM Compression
Beijing Institute of Technology, Zhuhai · Shenzhen University · Osaka University
One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression
Aug 19, 2025Mikołaj Janusz, Tomasz Wojnar, Yawei Li +2Model Compression Structured Pruning
aJagiellonian University · bETH Zürich · cWrocław University of Science and Technology
DarwinLM: Evolutionary Structured Pruning of Large Language Models
Feb 11, 2025Shengkun Tang, Oliver Sieberling, Eldar Kurtic +2LLM Pruning Structured Pruning
MBZUAI · ETH Zurich · ISTA +1

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices

Wentao Yue, Tianyou Lai, Hongji Li, Qingyu Mao+1

Non-IID Federated Learning Structured Pruning Federated Learning Heterogeneous Federated Learning

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Federated learning (FL) on heterogeneous edge devices must jointly accommodate unequal resource budgets and domain-shifted local data. Existing resource-adaptive methods decide how much of a model each client trains but not where retained capacity should reside or how it should be shared, whereas federated domain-generalization methods usually assume a shared full architecture. Uniform compression can therefore discard high-utility channels, and a single aggregation path can mix transferable features with domain-sensitive updates. We propose FedSAP, a domain-aware heterogeneous FL framework that casts structured pruning as budget-constrained tri-state channel allocation. FedSAP converts each keep ratio into non-uniform layer budgets, assigns stable channels to a Global pool, useful domain-sensitive channels to pseudo-domain-specific Private pools, and low-utility channels to a Dropped state. This partition lets broadly useful features benefit from cross-client pooling while isolating domain-sensitive updates from incompatible clients. Domain-Guided Assignment infers pseudo-domains from shallow-gradient similarity, while Type-Matched Aggregation restricts each channel to its intended sharing scope. Across three random seeds, FedSAP reaches 76.00% and 72.67% mean global accuracy on Digits and Office-Caltech, exceeding the strongest baseline by 1.70 and 4.92 percentage points while supporting client pruning ratios of up to 80% across heterogeneous clients.

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