Vision-Language Models for Edge Networks: A Comprehensive Survey | Paper Citation Record

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This paper cites Oscar: Object-semantics aligned pre-training for vision-language tasks,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Oscar: Object-semantics aligned pre-training for vision-language tasks,

Reference 2

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This paper cites Vilt: Vision-and-language transformer without convolution or region supervision,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Vilt: Vision-and-language transformer without convolution or region supervision,

Reference 3

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Vision-Language Models for Edge Networks: A Comprehensive Survey Edge intelligence empowered vehicular metaverse: Key design aspects and future directions,

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This paper cites Edge ai: On-demand accelerating deep neural network inference via edge computing,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Edge ai: On-demand accelerating deep neural network inference via edge computing,

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Vision-Language Models for Edge Networks: A Comprehensive Survey Lite trans- former with long-short range attention,

Reference 6

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This paper cites EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning.

Vision-Language Models for Edge Networks: A Comprehensive Survey EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning

Reference 8

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This paper cites Quantization and training of neural networks for effi- cient integer-arithmetic-only inference,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Quantization and training of neural networks for effi- cient integer-arithmetic-only inference,

Reference 10

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This paper cites Efficient processing of deep neural networks: A tutorial and survey,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Efficient processing of deep neural networks: A tutorial and survey,

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This paper cites Extended Insertion Functions for Opacity Enforcement.

Vision-Language Models for Edge Networks: A Comprehensive Survey Extended Insertion Functions for Opacity Enforcement

Reference 13

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This paper cites Deep learning for autonomous driving: Techniques and applications,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Deep learning for autonomous driving: Techniques and applications,

Reference 14

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This paper cites Real-time human activity recognition with miniaturized wearable sensors using deep learning,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Real-time human activity recognition with miniaturized wearable sensors using deep learning,

Reference 15

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This paper cites EfficientNetV2: Smaller Models and Faster Training.

Vision-Language Models for Edge Networks: A Comprehensive Survey EfficientNetV2: Smaller Models and Faster Training

Reference 16

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This paper cites Can deep learning revolutionize mobile sensing?.

Vision-Language Models for Edge Networks: A Comprehensive Survey Can deep learning revolutionize mobile sensing?

Reference 17

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This paper cites In-datacenter performance analysis of a tensor processing unit,.

Vision-Language Models for Edge Networks: A Comprehensive Survey In-datacenter performance analysis of a tensor processing unit,

Reference 18

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This paper cites Language mod- els are few-shot learners,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Language mod- els are few-shot learners,

Reference 19

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This paper cites Learning transferable visual models from natural language supervision,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Learning transferable visual models from natural language supervision,

Reference 20

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This paper cites Internet of things (iot) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5g-iot scenarios,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Internet of things (iot) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5g-iot scenarios,

Reference 21

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This paper cites Hardware acceleration for machine learning inference on edge devices: A review,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Hardware acceleration for machine learning inference on edge devices: A review,

Reference 22

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This paper cites Cloud and edge computing for deep learning applications,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Cloud and edge computing for deep learning applications,

Reference 23

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This paper cites Mnasnet: Platform-aware neural architecture search for mobile,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Mnasnet: Platform-aware neural architecture search for mobile,

Reference 24

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This paper cites Slimmable Neural Networks.

Vision-Language Models for Edge Networks: A Comprehensive Survey Slimmable Neural Networks

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This paper cites Attention is all you need,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Attention is all you need,

Reference 26

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This paper cites Transformer-based model for text classification and question answering,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Transformer-based model for text classification and question answering,

Reference 27

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Vision-Language Models for Edge Networks: A Comprehensive Survey Squeeze-and-excitation networks,

Reference 28

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Vision-Language Models for Edge Networks: A Comprehensive Survey Searching for mobilenetv3,

Reference 29

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This paper cites Dermatologist-level classification of skin cancer with deep neural networks,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Dermatologist-level classification of skin cancer with deep neural networks,

Reference 30

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This paper cites High-performance medicine: The convergence of human and artificial intelligence,.

Vision-Language Models for Edge Networks: A Comprehensive Survey High-performance medicine: The convergence of human and artificial intelligence,

Reference 31

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This paper cites Faster r-cnn: Towards real- time object detection with region proposal networks,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Faster r-cnn: Towards real- time object detection with region proposal networks,

Reference 32

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Vision-Language Models for Edge Networks: A Comprehensive Survey Ssd: Single shot multibox detector,

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This paper cites A survey of vision- language pre-trained models,.

Vision-Language Models for Edge Networks: A Comprehensive Survey A survey of vision- language pre-trained models,

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Vision-Language Models for Edge Networks: A Comprehensive Survey Vision-Language Intelligence: Tasks, Representation Learning, and Large Models

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This paper cites A survey of efficient fine-tuning methods for vision-language models prompt and adapter,.

Vision-Language Models for Edge Networks: A Comprehensive Survey A survey of efficient fine-tuning methods for vision-language models prompt and adapter,

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This paper cites Exploring the frontier of vision-language models: A survey of current methodologies and future directions,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Exploring the frontier of vision-language models: A survey of current methodologies and future directions,

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This paper cites Vision-language models for vision tasks: A survey,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Vision-language models for vision tasks: A survey,

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Vision-Language Models for Edge Networks: A Comprehensive Survey A survey on multimodal large language models for autonomous driving,

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Vision-Language Models for Edge Networks: A Comprehensive Survey A Survey on Multimodal Large Language Models

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Vision-Language Models for Edge Networks: A Comprehensive Survey Artificial intelligence market by offering, technology, end-user industry and geography - global forecast to 2026,

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Vision-Language Models for Edge Networks: A Comprehensive Survey Design choices for vision language models in 2024,

Reference 43

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Vision-Language Models for Edge Networks: A Comprehensive Survey Edge ai hardware market by device type, processor type, end user, and application: Global opportunity analysis and industry forecast, 20182025,

Reference 44

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Vision-Language Models for Edge Networks: A Comprehensive Survey Attention Is All You Need

Reference 45

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Vision-Language Models for Edge Networks: A Comprehensive Survey An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Vision-Language Models for Edge Networks: A Comprehensive Survey VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 47

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Vision-Language Models for Edge Networks: A Comprehensive Survey VL-BERT: Pre-training of Generic Visual-Linguistic Representations

Reference 48

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Vision-Language Models for Edge Networks: A Comprehensive Survey ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Reference 49

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Vision-Language Models for Edge Networks: A Comprehensive Survey MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

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Vision-Language Models for Edge Networks: A Comprehensive Survey Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks

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Vision-Language Models for Edge Networks: A Comprehensive Survey Vitamin: Designing scalable vision models in the vision-language era,

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Vision-Language Models for Edge Networks: A Comprehensive Survey Driving with language: Introducing wayves multimodal driving model lingo-2,

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Vision-Language Models for Edge Networks: A Comprehensive Survey Instructblip: Towards general-purpose vision-language models with instruction tuning,

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Vision-Language Models for Edge Networks: A Comprehensive Survey RAVEN: Multitask Retrieval Augmented Vision-Language Learning

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Vision-Language Models for Edge Networks: A Comprehensive Survey Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model

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Vision-Language Models for Edge Networks: A Comprehensive Survey ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models

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Vision-Language Models for Edge Networks: A Comprehensive Survey Learning Transferable Visual Models From Natural Language Supervision

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Vision-Language Models for Edge Networks: A Comprehensive Survey ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

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Vision-Language Models for Edge Networks: A Comprehensive Survey LXMERT: Learning Cross-Modality Encoder Representations from Transformers

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This paper cites A Traversable Wormhole from the Kerr Black Hole.

Vision-Language Models for Edge Networks: A Comprehensive Survey A Traversable Wormhole from the Kerr Black Hole

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This paper cites Learning to Prompt for Vision-Language Models.

Vision-Language Models for Edge Networks: A Comprehensive Survey Learning to Prompt for Vision-Language Models

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Vision-Language Models for Edge Networks: A Comprehensive Survey The Power of Scale for Parameter-Efficient Prompt Tuning

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Vision-Language Models for Edge Networks: A Comprehensive Survey P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

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This paper cites DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting.

Vision-Language Models for Edge Networks: A Comprehensive Survey DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting

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This paper cites The Evolving Path of "the Right to Be Left Alone" - When Privacy Meets Technology.

Vision-Language Models for Edge Networks: A Comprehensive Survey The Evolving Path of "the Right to Be Left Alone" - When Privacy Meets Technology

Reference 71

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This paper cites Parameter-Efficient Transfer Learning for NLP.

Vision-Language Models for Edge Networks: A Comprehensive Survey Parameter-Efficient Transfer Learning for NLP

Reference 72

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Observation 00f075db-2b9e-41f5-b05f-12c80708d2a7 · outbound

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Vision-Language Models for Edge Networks: A Comprehensive Survey LoRA: Low-Rank Adaptation of Large Language Models

Reference 73

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Observation 59f9992b-24f1-46f2-aadc-5f4064ef96c2 · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

Vision-Language Models for Edge Networks: A Comprehensive Survey AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 74

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Observation 78e543e1-6f7b-42c5-b022-36120f6f1271 · outbound

This paper cites The Growth of Protoplanets via the Accretion of Small Bodies in Disks Perturbed by the Planetary Gravity.

Vision-Language Models for Edge Networks: A Comprehensive Survey The Growth of Protoplanets via the Accretion of Small Bodies in Disks Perturbed by the Planetary Gravity

Reference 75

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Observation 7b45b557-9968-4cf6-a4f2-b84585d32935 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Clip-adapter: Better vision-language models with feature adapters,

Reference 76

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This paper cites Self-adapting large visual-language models to edge devices across visual modalities,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Self-adapting large visual-language models to edge devices across visual modalities,

Reference 77

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Vision-Language Models for Edge Networks: A Comprehensive Survey Resource optimized network virtualization empowered metaverse for wireless networks,

Reference 78

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This paper cites Survey on intelligence edge computing in 6g: characteristics, challenges, potential use cases, and market drivers,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Survey on intelligence edge computing in 6g: characteristics, challenges, potential use cases, and market drivers,

Reference 79

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This paper cites Authentication in mobile cloud computing: a survey,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Authentication in mobile cloud computing: a survey,

Reference 80

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This paper cites Karthikeyan and M.

Vision-Language Models for Edge Networks: A Comprehensive Survey Karthikeyan and M

Reference 81

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This paper cites Edge cloud computing technologies for internet of things: a primer,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Edge cloud computing technologies for internet of things: a primer,

Reference 82

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Vision-Language Models for Edge Networks: A Comprehensive Survey Sdn enhanced multi-access edge computing (mec) for e2e mobility and qos management,

Reference 83

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Vision-Language Models for Edge Networks: A Comprehensive Survey Edge computing security: state of the art and challenges,

Reference 84

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Vision-Language Models for Edge Networks: A Comprehensive Survey Efficient Prompting for LLM-based Generative Internet of Things

Reference 85

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Vision-Language Models for Edge Networks: A Comprehensive Survey LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution

Reference 86

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This paper cites Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities.

Vision-Language Models for Edge Networks: A Comprehensive Survey Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities

Reference 87

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Observation 190e0b48-77f3-41d9-be87-eea3076537b3 · outbound

This paper cites MiniVLM: A Smaller and Faster Vision-Language Model.

Vision-Language Models for Edge Networks: A Comprehensive Survey MiniVLM: A Smaller and Faster Vision-Language Model

Reference 88

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This paper cites Lightvlp: A lightweight vision-language pre-training via gated interactive masked autoencoders,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Lightvlp: A lightweight vision-language pre-training via gated interactive masked autoencoders,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-08T12:20:08.445239Z

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Observation 7faeb2bf-5ab6-4433-a29b-82bf6150213e · outbound

This paper cites Tiny vlms bring ai text plus image vision to the edge,.

Vision-Language Models for Edge Networks: A Comprehensive Survey Tiny vlms bring ai text plus image vision to the edge,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-08T12:20:08.450465Z

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Observation 229832af-0b80-48e4-9f71-cbd0c6446fa8 · outbound

This paper cites VILA: On Pre-training for Visual Language Models.

Vision-Language Models for Edge Networks: A Comprehensive Survey VILA: On Pre-training for Visual Language Models

Reference 91

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Observation 265d87f0-0bfe-476f-a8a2-f312a8279205 · outbound

This paper cites EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting.

Vision-Language Models for Edge Networks: A Comprehensive Survey EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-08T12:20:08.460205Z

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Vision-Language Models for Edge Networks: A Comprehensive Survey Patch Slimming for Efficient Vision Transformers

Reference 93

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:20:10.439394Z

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Observation 8d97dcd5-800a-45eb-9f3c-f26cdaa314f0 · outbound

This paper cites Data Selection for Efficient Model Update in Federated Learning.

Vision-Language Models for Edge Networks: A Comprehensive Survey Data Selection for Efficient Model Update in Federated Learning

Reference 94

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Observation 87393db9-496d-4b88-af6f-10cb9fe9f4b9 · outbound

This paper cites A systematic literature review on the use of federated learning and bioinspired computing,.

Vision-Language Models for Edge Networks: A Comprehensive Survey A systematic literature review on the use of federated learning and bioinspired computing,

Reference 95

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Vision-Language Models for Edge Networks: A Comprehensive Survey Enhancing edge-assisted federated learning with asynchronous aggregation and cluster pairing,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-08T12:20:08.479684Z

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This paper cites A multi-dimensional reverse auction mechanism for volatile federated learning in the mobile edge computing systems,.

Vision-Language Models for Edge Networks: A Comprehensive Survey A multi-dimensional reverse auction mechanism for volatile federated learning in the mobile edge computing systems,

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-08T12:20:08.483676Z

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Vision-Language Models for Edge Networks: A Comprehensive Survey MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 98

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Vision-Language Models for Edge Networks: A Comprehensive Survey Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 99

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Vision-Language Models for Edge Networks: A Comprehensive Survey Deep residual learning for image recognition,

Reference 100

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Vision-Language Models for Edge Networks: A Comprehensive Survey Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 101

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Vision-Language Models for Edge Networks: A Comprehensive Survey Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 102

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Vision-Language Models for Edge Networks: A Comprehensive Survey Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-08T12:20:08.508976Z

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Vision-Language Models for Edge Networks: A Comprehensive Survey Distilling the Knowledge in a Neural Network

Reference 104

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Vision-Language Models for Edge Networks: A Comprehensive Survey Neural architecture search: A survey,

Reference 105

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Vision-Language Models for Edge Networks: A Comprehensive Survey Large Batch Training of Convolutional Networks

Reference 106

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Vision-Language Models for Edge Networks: A Comprehensive Survey Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-08T12:20:08.525355Z

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Observation 19d75416-bb1f-4ce0-836f-9d981da8c89c · outbound

This paper cites Comprehensive Survey of Model Compression and Speed up for Vision Transformers.

Vision-Language Models for Edge Networks: A Comprehensive Survey Comprehensive Survey of Model Compression and Speed up for Vision Transformers

Reference 108

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:20:10.346207Z

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Observation 16fb22b1-2d96-4370-bf64-7d03aee16e5d · outbound

This paper cites Structured Pruning Learns Compact and Accurate Models.

Vision-Language Models for Edge Networks: A Comprehensive Survey Structured Pruning Learns Compact and Accurate Models

Reference 109

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Observation 798c597f-bd2d-4197-9643-addbca8a0705 · outbound

This paper cites Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks.

Vision-Language Models for Edge Networks: A Comprehensive Survey Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks

Reference 110

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:20:10.310348Z

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