{"total":115,"rows":[{"id":"2407.18921","title":"Mobile Edge Intelligence for Large Language Models: A Contemporary Survey","created":"2024-07-09","cited_by_count":128,"area":null},{"id":"2106.05022","title":"Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions","created":"2021-06-09","cited_by_count":75,"area":"inference"},{"id":"2301.01824","title":"Privacy and Efficiency of Communications in Federated Split Learning","created":"2023-01-04","cited_by_count":21,"area":null},{"id":"2205.11269","title":"Dynamic Split Computing for Efficient Deep Edge Intelligence","created":"2022-05-23","cited_by_count":18,"area":null},{"id":"2306.02652","title":"Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity","created":"2023-06-05","cited_by_count":16,"area":"inference"},{"id":"2203.08875","title":"SC2 Benchmark: Supervised Compression for Split Computing","created":"2022-03-16","cited_by_count":15,"area":null},{"id":"2204.04705","title":"SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems","created":"2022-04-10","cited_by_count":14,"area":null},{"id":"2505.17604","title":"Adaptive Semantic Token Communication for Transformer-based Edge Inference","created":"2025-05-23","cited_by_count":14,"area":null},{"id":"2309.09195","title":"SplitEE: Early Exit in Deep Neural Networks with Split Computing","created":"2023-09-17","cited_by_count":13,"area":"inference"},{"id":"2302.10681","title":"FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing","created":"2023-02-21","cited_by_count":12,"area":null},{"id":"2206.04864","title":"Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction","created":"2022-06-10","cited_by_count":11,"area":null},{"id":"2206.07269","title":"Resource-Constrained Edge AI with Early Exit Prediction","created":"2022-06-15","cited_by_count":11,"area":"inference"},{"id":"2403.07965","title":"Conditional computation in neural networks: principles and research trends","created":"2024-03-12","cited_by_count":10,"area":"moe"},{"id":"2504.02862","title":"Towards Understanding How Knowledge Evolves in Large Vision-Language Models","created":"2025-03-31","cited_by_count":10,"area":"mllm"},{"id":"2112.15131","title":"Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques","created":"2021-12-30","cited_by_count":9,"area":null},{"id":"2207.13050","title":"Efficient High-Resolution Deep Learning: A Survey","created":"2022-07-26","cited_by_count":9,"area":null},{"id":"2401.10541","title":"I-SplitEE: Image classification in Split Computing DNNs with Early Exits","created":"2024-01-19","cited_by_count":9,"area":null},{"id":"2107.06304","title":"Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks","created":"2021-07-13","cited_by_count":8,"area":null},{"id":"2209.11607","title":"I-SPLIT: Deep Network Interpretability for Split Computing","created":"2022-09-23","cited_by_count":7,"area":null},{"id":"2210.01625","title":"Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model","created":"2022-10-04","cited_by_count":7,"area":null},{"id":"2203.04516","title":"Update Compression for Deep Neural Networks on the Edge","created":"2022-03-09","cited_by_count":6,"area":null},{"id":"2310.14651","title":"$\\Lambda$-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI","created":"2023-10-23","cited_by_count":6,"area":"diffusion"},{"id":"2403.16677","title":"FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression","created":"2024-03-25","cited_by_count":6,"area":null},{"id":"2501.01420","title":"A Multi-task Supervised Compression Model for Split Computing","created":"2025-01-02","cited_by_count":6,"area":null},{"id":"2408.01890","title":"Cross-layer Attention Sharing for Pre-trained Large Language Models","created":"2024-08-04","cited_by_count":5,"area":"inference"},{"id":"2411.18199","title":"Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges","created":"2024-11-27","cited_by_count":5,"area":null},{"id":"2412.10448","title":"Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction","created":"2024-12-11","cited_by_count":5,"area":"diffusion"},{"id":"2501.07451","title":"A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion","created":"2025-01-13","cited_by_count":5,"area":null},{"id":"2511.21669","title":"DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Serving","created":"2025-11-26","cited_by_count":5,"area":"inference"},{"id":"2401.12167","title":"Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder","created":"2024-01-19","cited_by_count":4,"area":"deeprl"},{"id":"2511.18151","title":"AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems","created":"2025-11-22","cited_by_count":4,"area":null},{"id":"2601.05741","title":"ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers","created":"2026-01-09","cited_by_count":4,"area":null},{"id":"2208.13968","title":"Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing","created":"2022-08-30","cited_by_count":3,"area":null},{"id":"2301.12168","title":"Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits","created":"2023-01-28","cited_by_count":3,"area":null},{"id":"2303.12524","title":"Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications","created":"2023-03-22","cited_by_count":3,"area":null},{"id":"2304.08400","title":"ATHEENA: A Toolflow for Hardware Early-Exit Network Automation","created":"2023-04-17","cited_by_count":3,"area":null},{"id":"2306.12691","title":"Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems","created":"2023-06-22","cited_by_count":3,"area":null},{"id":"2310.13384","title":"Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing","created":"2023-10-20","cited_by_count":3,"area":null},{"id":"2311.18098","title":"Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels","created":"2023-11-29","cited_by_count":3,"area":"inference"},{"id":"2403.17154","title":"On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance","created":"2024-03-25","cited_by_count":3,"area":null},{"id":"2404.05241","title":"LightFF: Lightweight Inference for Forward-Forward Algorithm","created":"2024-04-08","cited_by_count":3,"area":null},{"id":"2410.05338","title":"Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach","created":"2024-10-06","cited_by_count":3,"area":"inference"},{"id":"2410.08673","title":"SpikeBottleNet: Spike-Driven Feature Compression Architecture for Edge-Cloud Co-Inference","created":"2024-10-11","cited_by_count":3,"area":null},{"id":"2509.11724","title":"DRAG: Data Reconstruction Attack using Guided Diffusion","created":"2025-09-15","cited_by_count":3,"area":"diffusion"},{"id":"2511.04002","title":"Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing","created":"2025-11-06","cited_by_count":3,"area":null},{"id":"2601.00644","title":"FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding","created":"2026-01-02","cited_by_count":3,"area":"inference"},{"id":"2305.05238","title":"Architectural Vision for Quantum Computing in the Edge-Cloud Continuum","created":"2023-05-09","cited_by_count":2,"area":null},{"id":"2305.14094","title":"Sustainable Edge Intelligence Through Energy-Aware Early Exiting","created":"2023-05-23","cited_by_count":2,"area":null},{"id":"2309.17401","title":"Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing","created":"2023-09-29","cited_by_count":2,"area":null},{"id":"2405.12705","title":"Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting","created":"2024-05-21","cited_by_count":2,"area":"inference"},{"id":"2407.05982","title":"MTL-Split: Multi-Task Learning for Edge Devices using Split Computing","created":"2024-07-08","cited_by_count":2,"area":null},{"id":"2410.12221","title":"EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge","created":"2024-10-16","cited_by_count":2,"area":"deeprl"},{"id":"2410.16723","title":"Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networks","created":"2024-10-22","cited_by_count":2,"area":null},{"id":"2410.18919","title":"Optimizing Edge Offloading Decisions for Object Detection","created":"2024-10-24","cited_by_count":2,"area":null},{"id":"2411.10500","title":"Edge-Only Universal Adversarial Attacks in Distributed Learning","created":"2024-11-15","cited_by_count":2,"area":null},{"id":"2505.16502","title":"Recursive Offloading for LLM Serving in Multi-tier Networks","created":"2025-05-22","cited_by_count":2,"area":"inference"},{"id":"2507.07954","title":"Input Conditioned Layer Dropping in Speech Foundation Models","created":"2025-07-10","cited_by_count":2,"area":null},{"id":"2509.15058","title":"Communication Efficient Split Learning of ViTs with Attention-based Double Compression","created":"2025-09-18","cited_by_count":2,"area":null},{"id":"2510.13714","title":"DeDelayed: Deleting Remote Inference Delay via On-Device Correction","created":"2025-10-15","cited_by_count":2,"area":null},{"id":"2111.11856","title":"Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks","created":"2021-11-15","cited_by_count":1,"area":null},{"id":"2206.05267","title":"CONTINUER: Maintaining Distributed DNN Services During Edge Failures","created":"2022-04-25","cited_by_count":1,"area":null},{"id":"2403.00942","title":"Resilience of Entropy Model in Distributed Neural Networks","created":"2024-03-01","cited_by_count":1,"area":null},{"id":"2405.17376","title":"Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients","created":"2024-05-27","cited_by_count":1,"area":"inference"},{"id":"2406.13086","title":"NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation","created":"2024-06-18","cited_by_count":1,"area":null},{"id":"2407.01563","title":"NaviSlim: Adaptive Context-Aware Navigation and Sensing via Dynamic Slimmable Networks","created":"2024-05-16","cited_by_count":1,"area":null},{"id":"2407.11763","title":"Enhancing Split Computing and Early Exit Applications through Predefined Sparsity","created":"2024-07-16","cited_by_count":1,"area":null},{"id":"2411.02471","title":"Energy-Aware Dynamic Neural Inference","created":"2024-11-04","cited_by_count":1,"area":null},{"id":"2504.13736","title":"LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networks","created":"2025-04-18","cited_by_count":1,"area":null},{"id":"2506.20094","title":"MEL: Multi-level Ensemble Learning for Resource-Constrained Environments","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2506.21453","title":"Towards an Optimal Control Perspective of ResNet Training","created":"2025-06-26","cited_by_count":1,"area":null},{"id":"2508.10598","title":"Oops!... They Stole it Again: Attacks on Split Learning","created":"2025-08-14","cited_by_count":1,"area":null},{"id":"2509.17885","title":"Confidence-gated training for efficient early-exit neural networks","created":"2025-09-22","cited_by_count":1,"area":"inference"},{"id":"2510.16747","title":"An Efficient Semantic Segmentation Decoder for In-Car or Distributed Applications","created":"2025-10-19","cited_by_count":1,"area":null},{"id":"2511.02293","title":"3D Point Cloud Object Detection on Edge Devices for Split Computing","created":"2025-11-04","cited_by_count":1,"area":null},{"id":"2511.11608","title":"Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2605.24025","title":"Towards Large Model Feature Coding","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2606.30919","title":"Budget-Adaptive Routing: Skipping the Weak When the Strong Answers Anyway","created":"2026-06-29","cited_by_count":1,"area":null},{"id":"2607.13093","title":"Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models","created":"2026-07-14","cited_by_count":1,"area":"inference"},{"id":"2608.15410","title":"FloodReasonBench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge","created":"2026-08-15","cited_by_count":1,"area":"lr"},{"id":"2105.02019","title":"ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer","created":"2021-05-05","cited_by_count":0,"area":null},{"id":"2110.02724","title":"ParaDiS: Parallelly Distributable Slimmable Neural Networks","created":"2021-10-06","cited_by_count":0,"area":null},{"id":"2209.08625","title":"Improving the Performance of DNN-based Software Services using Automated Layer Caching","created":"2022-09-18","cited_by_count":0,"area":"inference"},{"id":"2309.03530","title":"Efficient Single Object Detection on Image Patches with Early Exit Enhanced High-Precision CNNs","created":"2023-09-07","cited_by_count":0,"area":null},{"id":"2310.01259","title":"SINF: Semantic Neural Network Inference with Semantic Subgraphs","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2402.06580","title":"SAE: Single Architecture Ensemble Neural Networks","created":"2024-02-09","cited_by_count":0,"area":null},{"id":"2405.04249","title":"Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks","created":"2024-05-07","cited_by_count":0,"area":"inference"},{"id":"2405.09077","title":"Compressive Feature Selection for Remote Visual Multi-Task Inference","created":"2024-05-15","cited_by_count":0,"area":null},{"id":"2412.16616","title":"Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach","created":"2024-12-21","cited_by_count":0,"area":null},{"id":"2503.24014","title":"Optimization of Layer Skipping and Frequency Scaling for Convolutional Neural Networks under Latency Constraint","created":"2025-03-31","cited_by_count":0,"area":null},{"id":"2507.07259","title":"Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2507.16594","title":"Optimizing Split Learning Latency in TinyML-Based IoT Systems","created":"2025-07-22","cited_by_count":0,"area":null},{"id":"2508.20613","title":"Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2508.20991","title":"ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2509.08318","title":"CalexNet: Soft Cascade-Aligned Training and Calibration for Lightweight Early-Exit Branches","created":"2025-09-10","cited_by_count":0,"area":"inference"},{"id":"2509.22992","title":"T-TAMER: Provably Taming Trade-offs in ML Serving","created":"2025-09-26","cited_by_count":0,"area":"inference"},{"id":"2511.12592","title":"Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHC","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2601.07119","title":"SC-MII: Infrastructure LiDAR-based 3D Object Detection on Edge Devices for Split Computing with Multiple Intermediate Outputs Integration","created":"2026-01-12","cited_by_count":0,"area":null},{"id":"2601.16117","title":"Distillation-based Layer Dropping (DLD): Effective End-to-end Framework for Dynamic Speech Networks","created":"2026-01-22","cited_by_count":0,"area":null},{"id":"2601.22002","title":"Understanding Rate-Distortion Performance in Distributed Transformer Inference","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.16760","title":"Privacy-Aware Split Inference with Speculative Decoding for Large Language Models over Wide-Area Networks","created":"2026-02-18","cited_by_count":0,"area":"inference"}],"next_cursor":"100"}